System and method for configuring boot-time parameters of nodes of a  cloud computing system

ABSTRACT

The present disclosure relates to a method and system for configuring a computing system, such as a cloud computing system. A method includes providing a user interface comprising selectable boot-time configuration data and selecting, based on at least one user selection of the boot-time configuration data, a boot-time configuration for at least one node of a cluster of nodes of the computing system. The method further includes configuring the at least one node of the cluster of nodes with the selected boot-time configuration to modify at least one boot-time parameter of the at least one node.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending applications,all owned by the instant assignee and filed on even date herewith: U.S.application Ser. No. ______ entitled “SYSTEM AND METHOD FOR ALLOCATING ACLUSTER OF NODES FOR A CLOUD COMPUTING SYSTEM BASED ON HARDWARECHARACTERISTICS,” docket number 100100.110237, inventors MauricioBreternitz et al.; U.S. application Ser. No. ______ entitled “SYSTEM ANDMETHOD FOR TUNING A CLOUD COMPUTING SYSTEM,” docket number100100.110238, inventors Mauricio Breternitz et al.; U.S. applicationSer. No. ______ entitled “SYSTEM AND METHOD FOR EMULATING A DESIREDNETWORK CONFIGURATION IN A CLOUD COMPUTING SYSTEM,” docket number100100.110239, inventors Mauricio Breternitz et al.; U.S. applicationSer. No. ______ entitled “SYSTEM AND METHOD FOR MODIFYING A HARDWARECONFIGURATION OF A CLOUD COMPUTING SYSTEM,” docket number 100100.110240,inventors Mauricio Breternitz et al.; U.S. application Ser. No. ______entitled “SYSTEM AND METHOD FOR CONFIGURING A CLOUD COMPUTING SYSTEMWITH A SYNTHETIC TEST WORKLOAD,” docket number 100100.110241, inventorsMauricio Breternitz et al.; and U.S. application Ser. No. ______entitled “SYSTEM AND METHOD FOR CONFIGURING CLOUD COMPUTING SYSTEMS,”docket number 100100.110236, inventors Mauricio Breternitz et al., thedisclosures of which are all expressly incorporated by reference herein.

FIELD OF THE DISCLOSURE

The present disclosure is generally related to the field of computingsystems, and more particularly to methods and systems for configuring aboot-time configuration of at least one node of a cluster of nodes of acloud computing system.

BACKGROUND

Cloud computing involves the delivery of hosted services over a network,such as the Internet, for example. Cloud computing systems provide forthe delivery of computing capacity and storage capacity as a service toend users. Cloud computing systems include multiple servers, or “nodes”,operating on a distributed communication network, and each node includeslocal processing capability and memory. For example, each node of thecloud computing system includes at least one processing device forproviding computing capability and a memory for providing storagecapacity. Rather than running an application locally or storing datalocally, a user may run the application or store data remotely on thecloud or “cluster” of nodes. End users may access cloud-basedapplications through a web browser or some other software application ona local computer, for example, while the software application and/ordata related to the software application are stored and/or executed onthe cloud nodes at a remote location. Cloud computing resources aretypically allocated to the end user on demand, with the cloud computingsystem cost corresponding to the actual amount of resources utilized bythe end user.

Computing tasks are distributed across multiple nodes of the cloudcomputing system in the form of a workload. The nodes operate to shareprocessing of the workload. A workload (also referred to as a “kernel”)includes a computing job or task that is performed and executed on thecloud of nodes. A workload, which comprises a collection of software orfirmware code and any necessary data, includes any application orprogram or a portion of an application or program that is executed onthe cluster of nodes. For example, one exemplary workload is anapplication that implements one or more algorithms. Exemplary algorithmsinclude, for example, clustering, sorting, classifying, or filtering adataset. Other exemplary workloads include service-oriented applicationsthat are executed to provide a computing service to an end-user. In someembodiments, a workload includes a single application that is cloned andexecuted on multiple nodes simultaneously. A load balancer distributesrequests to be executed with the workload across the cluster of nodessuch that the nodes share the processing load associated with theworkload. The cluster of nodes collaborates results of an execution ofthe workload to produce a final result.

A workload container, which comprises one or more processors of a nodeexecuting a workload container module (e.g., software or firmware code),operates on each node. The workload container is an execution frameworkfor workloads to provide a software environment that initiates andorchestrates the execution of workloads on a cluster of nodes. Workloadcontainers typically provide an execution framework for a particularclass of workloads on the cluster of nodes. The workload containerconfigures the associated node to operate as a node of the cloud suchthat the node executes the workload, shares the results of the workloadexecution with other nodes of the cloud, and collaborates andcommunicates with other nodes of the cloud. In one embodiment, theworkload container includes application program interfaces (API's) orXML-based interfaces for interfacing with other nodes as well as withother applications and hardware of the associated node.

One exemplary workload container is Apache Hadoop, which is Java-based,that provides a map-reduce framework and a distributed file system(HDFS) for map-reduce workloads. A cluster of nodes operating with theHadoop workload container typically includes a master node as well asmultiple worker nodes. The Hadoop workload container coordinates theassignment of the master or worker status to each node and informs eachnode that it is operating in a cloud. The master node tracks job (i.e.,workload) initiation and completion as well as file system metadata. Inthe “map” phase of the map-reduce framework, a task or workload ispartitioned into multiple portions (i.e., multiple groups of one or moreprocessing threads), and the portions of the workload are distributed tothe worker nodes that process the threads and the associated input data.In the “reduce” phase, the output from each worker node is collected andcombined to produce a final result or answer. The distributed filesystem (HDFS) of Hadoop is utilized to store data and to communicatedata between the worker nodes. The HDFS file system supports datareplication to increase the likelihood of data reliability by storingmultiple copies of the data and files.

Setting up or configuring a cluster of nodes in prior art cloudcomputing platforms is a complex process that requires a steep learningcurve. The cloud software and workloads must be individually deployed toeach node, and any configuration changes must also be deployed to eachnode individually. Analyzing the performance of the cluster of nodes andoptimizing the cloud set-up involves multiple independent variables andis often time-consuming, requiring ad-hoc interfaces adapted formonitoring and analyzing particular applications. In particular, thecloud operator or engineer must create commands to obtain data about howthe workload is running as well as to obtain the actual results of theworkload. Additionally, such data is in a format that is specific to thesystem configuration at hand, and the data must be integrated by thecloud operator or engineer in a form that is suitable for performanceanalysis. The cloud operator or engineer is required to learn specificdetails of the cloud mechanism, any networking issues, systemadministration-related tasks, as well as deployment and data formats ofthe available performance analysis tools. Further, monitoring andanalyzing performance of workloads on the cluster of nodes is complex,time consuming, and dependent on the particular cloud configuration. Thecloud operator or engineer is not always privy to all of theconfiguration and hardware information for the particular cloud system,making accurate performance analysis difficult.

Several cloud computing platforms are available today, including AmazonWeb Services (AWS) and OpenStack, for example. Amazon's AWS, whichincludes Elastic Compute Cloud (EC2), rents a cluster of nodes (servers)to an end-user for use as a cloud computing system. AWS allows the userto allocate a cluster of nodes and to execute a workload on the clusterof nodes. AWS limits the user to execute workloads only onAmazon-provided server hardware with various restrictions, such asrequiring specific hardware configurations and software configurations.OpenStack allows a user to build and manage a cluster of nodes onuser-provided hardware. AWS and OpenStack lack a mechanism for quicklyconfiguring and deploying workload and workload container software toeach node, for modifying network parameters, and for aggregatingperformance data from all nodes of the cluster.

A known method of testing the performance of a particular localprocessor includes creating a synthetic, binary code based onuser-specified parameters that can be executed by the local processor.However, generation of the binary synthetic code requires the user tohard-code the user-specified parameters, requiring significantdevelopment time and prior knowledge of the architecture of the targetprocessor. Such hard-coded synthetic code must be written to target aparticular instruction set architecture (ISA) (e.g. x86) and aparticular microarchitecture of the targeted processor. Instruction setarchitecture refers to the component of computer architecture thatidentifies data types/formats, instructions, data block size, processingregisters, memory addressing modes, memory architecture, interrupt andexception handling, I/O, etc. Microarchitecture refers to the componentof computer architecture that identifies the data paths, data processingelements (e.g., logic gates, arithmetic logic units (ALUs), etc.), datastorage elements (e.g., registers, cache, etc.), etc., and how theprocessor implements the instruction set architecture. As such, thesynthetic code must be re-engineered with modified or new hard-codedparameters and instructions to execute variations of an instruction setarchitecture and different microarchitectures of other processor(s). Assuch, such hard-coded synthetic code is not suitable for testingmultiple nodes of a cloud computing system.

Another method of testing the performance of a local processor is toexecute an industry-standard workload or trace, such as a workloadprovided by the Standard Performance Evaluation Corporation (SPEC), tocompare the processor's performance with a performance benchmark.However, executing the entire industry-standard workload often requireslarge amounts of simulation time. Extracting relevant, smaller tracesfrom the workload for execution by the processor may reduce simulationtime but also requires extra engineering effort to identify and extractthe relevant traces. Further, the selection of an industry-standardworkload, or the extraction of smaller traces from a workload, must berepeated for distinct architectural configurations of the processor(s).

Current cloud systems that deliver computing capacity and storagecapacity as a service to end users lack a mechanism to change theboot-time configuration of each node of the cluster of nodes of thecloud system. For example, boot-time configuration changes must behard-coded onto each node of the cloud by an engineer or programmer inorder to modify boot-time parameters of the nodes, which requiresconsiderable time and is cumbersome. Further, the engineer must havedetailed knowledge of the hardware and computer architecture of thecluster of node prior to writing the configuration code.

Typical cloud systems that deliver computing capacity and storagecapacity as a service to end users lack a mechanism to allow a user tospecify and to modify a network configuration of the allocated clusterof nodes. In many cloud systems, users can only request a general typeof nodes and have little or no direct control over the network topology,i.e., the physical and logical network connectivity of the nodes, andthe network performance characteristics of the requested nodes. AmazonAWS, for example, allows users to select nodes that are physicallylocated in a same general region of the country or world (e.g., Easternor Western United States, Europe, etc.), but the network connectivity ofthe nodes and the network performance characteristics of the nodes arenot selectable or modifiable. Further, some of the selected nodes may bephysically located far away from other selected nodes, despite being inthe same general region of the country or even in the same data center.For example, the nodes allocated by the cloud system may be located onseparate racks in a distributed data center that are physically farapart, resulting in decreased or inconsistent network performancebetween nodes.

Similarly, in typical cloud systems, the end user has limited or nocontrol over the actual hardware resources of the node cluster. Forexample, when allocating nodes, the user can only request nodes of ageneral type. Each available type of node may be classified by thenumber of the CPU(s) of the node, the available memory, available diskspace, and general region of the country or world where the node islocated. However, the allocated node may not have the exact hardwarecharacteristics as the selected node type. Selectable node types arecoarse classifications. For example, the node types may include small,medium, large, and extra large corresponding to the amount of systemmemory and disk space as well as the number of processing cores of thenode. However, even with nodes selected having a same general type, theactual computing capacity and storage capacity of the nodes allocated bythe system may vary. For example, the available memory and disk space aswell as operating frequency and other characteristics may vary or fallwithin a range of values. For example, a “medium” node may include anynode having a system memory of 1500 MB to 5000 MB and storage capacityof 200 GB to 400 GB. As such, the user is not always privy to the actualhardware configuration of the allocated nodes. Further, even among nodeshaving the same number of processors and memory/disk space, otherhardware characteristics of these nodes may vary. For example, similarnodes vary based on the operating frequency of the nodes, the size ofthe cache, a 32-bit architecture versus a 64-bit architecture, themanufacturer of the nodes, the instruction set architecture, etc., anduser has no control over these characteristics of the selected nodes.

Often the user does not have a clear understanding of the specifichardware resources required by his application or workload. Thedifficulty in setting up the node cluster to execute the workloadresults in the user having limited opportunity to try different hardwareconfigurations. Combined with the user's lack of knowledge of the actualhardware resources of the allocated nodes, this often results inunnecessary user costs for under-utilized hardware resources. Variousmonitoring tools are available that can measure the CPU, memory, anddisk and network utilization of a single physical processing machine.However, current cloud systems do not provide a mechanism to allow auser to deploy these monitoring tools to the nodes of the cluster tomonitor hardware usage. As such, actual hardware utilization duringworkload execution is unknown to the user. Most public cloud servicesoffer an accounting mechanism that can provide basic information aboutthe cost of the requested hardware resources used by the user whilerunning a workload. However, such mechanisms only provide basicinformation about the costs of the requested hardware resources, and donot identify the actual hardware resources used during workloadexecution.

In many cloud systems, a limited number of configuration parameters areavailable to the user for adjusting and improving a configuration of thenode cluster. For example, a user may only be able to select differentnodes having different general node types to alter the cloudconfiguration. Further, each configuration change must be implementedmanually by the user by selecting different nodes for the node clusterand starting the workload with the different nodes. Such manual effortto apply configuration changes and to test the results is costly andtime consuming. Further, the various performance monitoring tools thatare available for testing node performance are typically adapted for asingle physical processing machine, and current cloud systems lack amechanism to allow a user to deploy these monitoring tools to the nodesof the cluster to test performance of the node cluster with thedifferent configurations.

Therefore, a need exists for methods and systems for automating thecreation, deployment, provision, execution, and data aggregation ofworkloads on a node cluster of arbitrary size. A need further exists formethods and systems to quickly configure and deploy workload andworkload container software to each node and to aggregate and analyzeworkload performance data from all nodes of the cluster. A need furtherexists for methods and systems to test the performance of multiple nodesof a cloud computing system and to provide automated configurationtuning of the cloud computing system based on the monitored performance.A need further exists for methods and systems to generate retargetablesynthetic test workloads for execution on the cloud computing system fortesting node processors having various computer architectures. A needfurther exists for methods and systems that provide for the modificationof a boot-time configuration of nodes of a cloud computing system. Aneed further exists for methods and systems that facilitate themodification of a network configuration of the cluster of nodes of thecloud system. A need further exists for methods and systems that allowfor the automated selection of suitable nodes for the cluster of nodesbased on a desired network topology, a desired network performance,and/or a desired hardware performance of the cloud system. A needfurther exists for methods and systems to measure the usage of hardwareresources of the node cluster during workload execution and to providehardware usage feedback to a user and/or automatically modify the nodecluster configuration based on the monitored usage of the hardwareresources.

SUMMARY OF EMBODIMENTS OF THE DISCLOSURE

In an exemplary embodiment of the present disclosure, a method ofconfiguring a computing system carried out by one or more computingdevices is provided. The method includes providing a user interfacecomprising selectable boot-time configuration data. The method furtherincludes selecting, based on at least one user selection of theselectable boot-time configuration data, a boot-time configuration forat least one node of a cluster of nodes of the computing system. Eachnode of the cluster of nodes is operative to share processing of aworkload with other nodes of the cluster of nodes. The method furtherincludes configuring the at least one node of the cluster of nodes withthe selected boot-time configuration to modify at least one boot-timeparameter of the at least one node.

Among other advantages, some embodiments may allow for the selection andmodification of a boot-time configuration of one or more nodes of acluster of nodes of a computing system, such as a cloud computingsystem, based on desired boot-time parameters. In addition, someembodiments may provide for the deployment of the selected boot-timeconfiguration to the cluster of nodes of the computing system such thatthe cluster of nodes adjusts its boot-time parameters based on theselected boot-time configuration. Other advantages will be recognized bythose of ordinary skill in the art.

In another exemplary embodiment of the present disclosure, a computingconfiguration system is provided. The system includes a nodeconfigurator operative to select, based on at least one user selectionof selectable boot-time configuration data of a user interface, aboot-time configuration for at least one node of a cluster of nodes of acomputing system. The node configurator is further operative toconfigure the at least one node of the cluster of nodes with theselected boot-time configuration to modify at least one boot-timeparameter of the at least one node such that shared processing of aworkload by the cluster of nodes is based on the modified at least oneboot-time parameter.

In yet another exemplary embodiment of the present disclosure, a cloudcomputing configuration method carried out by a node of a cluster ofnodes of a cloud computing system is provided. The method includesmodifying, by the node of the cluster of nodes, at least one boot-timeparameter of the node based on a boot-time configuration adjustmentrequest provided by a cloud configuration server. Each node of thecluster of nodes includes at least one processing device and memory andis operative to share processing of a workload with other nodes of thecluster of nodes. The method further includes executing, by the nodefollowing a reboot of the node, at least a portion of the workload upona determination by the node following the reboot of the node that the atleast one boot-time parameter has been modified according to theboot-time configuration adjustment request.

In still another exemplary embodiment of the present disclosure, a cloudcomputing system is provided that includes a cloud configuration serveroperative to generate a boot-time configuration adjustment request. Thesystem further includes a cluster of nodes in communication with thecloud configuration server that is operative to execute a workload. Eachnode of the cluster of nodes includes at least one processing device andmemory and is operative to share processing of the workload with othernodes of the cluster of nodes. A node of the cluster of nodes isoperative to modify at least one boot-time parameter of the node basedon the boot-time configuration adjustment request provided with thecloud configuration server. The node is further operative to execute,following a reboot of the node by the node, at least a portion of theworkload upon a determination by the node following the reboot of thenode that the at least one boot-time parameter has been modifiedaccording to the boot-time configuration adjustment request.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more readily understood in view of the followingdescription when accompanied by the below figures and wherein likereference numerals represent like elements:

FIG. 1 is a block diagram of a cloud computing system in accordance withan embodiment including a cluster of nodes operating on a communicationnetwork, a control server in communication with the cluster of nodes,and a configurator of the control server;

FIG. 2 is a block diagram of an exemplary node of the cluster of nodesof FIG. 1 including at least one processor and a memory;

FIG. 3 is a block diagram of an exemplary control server of the cloudcomputing system of FIG. 1 including a configurator operative toconfigure the cloud computing system of FIG. 1;

FIG. 4 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for configuring a cloud computing system;

FIG. 5 is a flow chart of another exemplary method of operation of theconfigurator of FIG. 3 for configuring a cloud computing system;

FIG. 6 is a flow chart of another exemplary method of operation of theconfigurator of FIG. 3 for configuring a cloud computing system;

FIG. 7 illustrates an exemplary user interface provided by theconfigurator of FIG. 3 including an Authentication and Settings Librarymodule for facilitating user access authentication;

FIG. 8 illustrates an Instances module of the exemplary user interfaceof FIG. 7 including an Instances tab for facilitating the selection ofthe cluster of nodes of FIG. 1;

FIG. 9 illustrates an Instance Types tab of the Instances module of FIG.8 for facilitating the selection of a node type for nodes of the clusterof nodes of FIG. 1;

FIG. 10 illustrates an Other Instances Settings tab of the Instancesmodule of FIG. 8 for facilitating the configuration of boot-timeparameters of one or more nodes of the cluster of nodes of FIG. 1;

FIG. 11 illustrates a Network Settings Wizard of a Network Configurationmodule of the exemplary user interface of FIG. 7 including a Delay tabfor facilitating the implementation of a network delay on thecommunication network of FIG. 1;

FIG. 12 illustrates a Packet Loss tab of the Network Configurationmodule of FIG. 11 for facilitating the adjustment of a packet loss rateon the communication network of FIG. 1;

FIG. 13 illustrates a Packet Duplication tab of the NetworkConfiguration module of FIG. 11 for facilitating the adjustment of apacket duplication rate on the communication network of FIG. 1;

FIG. 14 illustrates a Packet Corruption tab of the Network Configurationmodule of FIG. 11 for facilitating the adjustment of a packet corruptionrate on the communication network of FIG. 1;

FIG. 15 illustrates a Packet Reordering tab of the Network Configurationmodule of FIG. 11 for facilitating the adjustment of a packet reorderingrate on the communication network of FIG. 1;

FIG. 16 illustrates a Rate Control tab of the Network Configurationmodule of FIG. 11 for facilitating the adjustment of a communicationrate on the communication network of FIG. 1;

FIG. 17 illustrates a Custom Commands tab of the Network Configurationmodule of FIG. 11 for facilitating the adjustment of network parameterson the communication network of FIG. 1 based on custom command strings;

FIG. 18 illustrates a Workload Container Configuration module of theexemplary user interface of FIG. 7 including a Hadoop tab forfacilitating the selection of a Hadoop workload container;

FIG. 19 illustrates the Hadoop tab of the Workload ContainerConfiguration module of FIG. 18 including an Extended tab forfacilitating the configuration of operational parameters of the Hadoopworkload container;

FIG. 20 illustrates the Hadoop tab of the Workload ContainerConfiguration module of FIG. 18 including a Custom tab for facilitatingthe configuration of operational parameters of the Hadoop workloadcontainer based on custom command strings;

FIG. 21 illustrates a Custom tab of the Workload Container Configurationmodule of FIG. 18 for facilitating the selection of a custom workloadcontainer;

FIG. 22 illustrates a Workload Configuration module of the exemplaryuser interface of FIG. 7 including a Workload tab for facilitating theselection of a workload for execution on the cluster of nodes of FIG. 1;

FIG. 23 illustrates a Synthetic Kernel tab of the Workload Configurationmodule of FIG. 22 for facilitating the configuration of a synthetic testworkload;

FIG. 24 illustrates a MC-Blaster tab of the Workload Configurationmodule of FIG. 22 for facilitating the configuration of a Memcachedworkload;

FIG. 25 illustrates a Batch Processing module of the exemplary userinterface of FIG. 7 for facilitating the selection and configuration ofa batch sequence for execution on the cluster of nodes of FIG. 1;

FIG. 26 illustrates a Monitoring module of the exemplary user interfaceof FIG. 7 including a Hadoop tab for facilitating the configuration of aHadoop data monitoring tool;

FIG. 27 illustrates a Ganglia tab of the Monitoring module of FIG. 26for facilitating the configuration of a Ganglia data monitoring tool;

FIG. 28 illustrates a SystemTap tab of the Monitoring module of FIG. 26for facilitating the configuration of a SystemTap data monitoring tool;

FIG. 29 illustrates an I/O Time tab of the Monitoring module of FIG. 26for facilitating the configuration of virtual memory statistics (VMStat)and input/output statistics (IOStat) data monitoring tools;

FIG. 30 illustrates a Control and Status module of the exemplary userinterface of FIG. 7 for facilitating the deployment of the systemconfiguration to the cluster of nodes of FIG. 1 and for facilitating theaggregation of data monitored by the monitoring tools of FIGS. 26-29;

FIG. 31 is another block diagram of the cloud computing system of FIG. 1illustrating a web-based data aggregator of the configurator of FIG. 1;

FIG. 32 illustrates an exemplary table illustrating a plurality ofuser-defined workload parameters for generating a synthetic testworkload;

FIG. 33 is a block diagram of an exemplary synthetic test workloadsystem including a synthesizer operative to generate the synthetic testworkload and a synthetic workload engine of a node operative to activateand execute at least a portion of the synthetic test workload;

FIG. 34 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for configuring a cloud computing system with atleast one of an actual workload and a synthetic test workload;

FIG. 35 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for configuring a cloud computing system with asynthetic test workload;

FIG. 36 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for selecting a boot-time configuration of atleast one node of the cluster of nodes of FIG. 1;

FIG. 37 is a flow chart of an exemplary method of operation of a node ofthe cluster of nodes of FIG. 1 for modifying at least one boot-timeparameter of the node;

FIG. 38 is a flow chart of an exemplary method of operation of the cloudcomputing system of FIG. 1 for modifying a boot-time configuration ofone or more nodes of the cluster of nodes of FIG. 1;

FIG. 39 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for modifying a communication networkconfiguration of at least one node of the cluster of nodes of FIG. 1;

FIG. 40 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for selecting a cluster of nodes for a cloudcomputing system based on a network configuration of an emulated nodecluster;

FIG. 41 is a flow chart of another exemplary method of operation of theconfigurator of FIG. 3 for selecting and configuring a cluster of nodesfor a cloud computing system based on a network configuration of anemulated node cluster;

FIG. 42 illustrates an exemplary data file that identifies a pluralityof communication network characteristics of a node cluster;

FIG. 43 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for selecting the cluster of nodes of FIG. 1;

FIG. 44 is a flow chart of another exemplary method of operation of theconfigurator of FIG. 3 for selecting the cluster of nodes of FIG. 1;

FIG. 45 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for selecting a hardware configuration of thecluster of nodes of FIG. 1;

FIG. 46 is a flow chart of another exemplary method of operation of theconfigurator of FIG. 3 for selecting a hardware configuration of thecluster of nodes of FIG. 1;

FIG. 47 is a flow chart of an exemplary method of operation of theconfigurator of FIG. 3 for selecting configuration parameters for thecluster of nodes of FIG. 1 based on monitored performancecharacteristics of the cluster of nodes; and

FIG. 48 is a flow chart of another exemplary method of operation of theconfigurator of FIG. 3 for selecting configuration parameters for thecluster of nodes of FIG. 1 based on monitored performancecharacteristics of the cluster of nodes.

DETAILED DESCRIPTION

While the embodiments disclosed herein are described with respect to acloud computing system, the methods and systems of the presentdisclosure may be implemented with any suitable computing system thatincludes multiple nodes cooperating to execute a workload.

As referenced herein, a node of a computing system includes at least oneprocessing device and a memory accessible by the at least one processingdevice. A node may also be referred to as a server, a virtual server, avirtual machine, an instance, or a processing node, for example.

FIG. 1 illustrates an exemplary cloud computing system 10 according tovarious embodiments that is configured to deliver computing capacity andstorage capacity as a service to end users. Cloud computing system 10includes a control server 12 operatively coupled to a cluster of nodes14. The cluster of nodes 14 is connected to a distributed communicationnetwork 18, and each node 16 includes local processing capability andmemory. In particular, each node 16 includes at least one processor 40(FIG. 2) and at least one memory 42 (FIG. 2) that is accessible by theprocessor 40. Communication network 18 includes any suitable computernetworking protocol, such as an internet protocol (IP) format includingTransmission Control Protocol/Internet Protocol (TCP/IP) or UserDatagram Protocol (UDP), an Ethernet network, a serial network, or otherlocal or wide area network (LAN or WAN), for example.

As described herein, nodes 16 are selected by control server 12 from acloud of multiple available nodes 16 connected on communication network18 to designate the cluster of nodes 14. The available nodes 16 areprovided on one or more server storage racks in a data center, forexample, and include a variety of hardware configurations. In oneembodiment, available nodes 16 from multiple data centers and/or otherhardware providers are accessible by control server 12 for selection andconfiguration as a cluster of nodes 14 for a cloud computing system 10.For example, one or more third-party data centers (e.g., Amazon WebServices, etc.) and/or user-provided hardware may be configured forcloud computing by control server 12. In one example, thousands of nodes16 may be available for selection and configuration by control server12, although any number of nodes 16 may be available. While five nodes16 are illustrated in FIG. 1, any suitable number of nodes 16 may beselected for cloud computing system 10. Control server 12 includes oneor more computing devices, illustratively server computers, eachincluding one or more processors. In the illustrated embodiment, controlserver 12 is a dedicated server computer 12 physically separate fromnode cluster 14. In one embodiment, control server 12 is physicallyremote from the data center housing the available nodes 16. Controlserver 12 alternatively may be one or more nodes 16 of the selectedcluster of nodes 14. Control server 12 serves as a cloud computingconfiguration system operative to allocate and configure nodes 16, tostart a workload on nodes 16, to collect and report performance data,etc., as described herein.

Control server 12 illustratively includes a configurator 22, a loadgenerator 24, and a load balancer 26. As referenced herein, configurator22, load generator 24, and load balancer 26 comprise one or moreprocessors that execute software or firmware code stored in an internalor external memory accessible by the one or more processors. Thesoftware/firmware code contains instructions corresponding to thefunctions of configurator 22, load generator 24, and load balancer 26that, when executed by the one or more processors, cause the one or moreprocessors to perform the functions described herein. Configurator 22,load generator 24, and/or load balancer 26 may alternatively includeapplication-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), digital signal processors (DSPs), hardwired logic,or combinations thereof. Configurator 22 is operative to select andconfigure one or more nodes 16 for inclusion in the cluster of nodes 14,to configure parameters of communication network 18, to select,configure, and deploy a workload container module and a workload forexecution on the cluster of nodes 14, and to gather and analyzeperformance data associated with the execution of the workload, asdescribed herein. Configurator 22 is operative to generate configurationfiles 28 that are provided to and processed at nodes 16 for configuringsoftware on nodes 16 and at least one configuration file 30 provided toload generator 24 for providing workload request parameters to loadgenerator 24.

Load generator 24 is operative to generate requests that serve as inputused by node cluster 14 for workload execution. In other words, nodecluster 14 executes the workload based on the requests and the inputparameters and data provided with the requests. In one embodiment, therequests from load generator 24 are initiated by a user. For example, auser or customer may request (e.g., via user interface 200) a search ora sort operation for a specified search term or dataset, respectively,and load generator 24 generates a corresponding search or sort request.In one embodiment, configurator 22 generates a configuration file 30that describes the user requests received via user interface 200. Nodes16 execute the workload using the identified terms to be searched or thedataset to be sorted. Load generator 24 may generator other suitablerequests depending on the type of workload to be executed. Load balancer26 is operative to distribute the requests provided by load generator 24among nodes 16 to direct which nodes 16 execute which requests. Loadbalancer 26 is also operative to divide a request from load generator 24into parts and to distribute the parts to nodes 16 such that multiplenodes 16 operate in parallel to execute the request.

Configurator 22 is illustratively web-based such that a user may accessconfigurator 22 over the Internet, although configurator 22 may beaccessed over any suitable network or communication link. An exemplaryuser's computer 20 is illustrated in FIG. 1 including a display 21, aprocessor 32 (e.g., central processing unit (CPU)), and a memory 34accessible by processor 32. Computer 20 may include any suitablecomputing device such as a desktop computer, a laptop, a mobile device,a smartphone, etc. A web-browser 36, which includes software or firmwarecode, is run on computer 20 and is used to access a graphical userinterface provided by configurator 22 and to display the graphical userinterface on display 21. See, for example, graphical user interface 200illustrated in FIGS. 7-30.

Various other arrangements of components and corresponding connectivityof cloud computing system 10, that are alternatives to what isillustrated in the figures, may be utilized and such arrangements ofcomponents and corresponding connectivity would remain in accordancewith the embodiments herein disclosed.

Referring to FIG. 2, an exemplary node 16 of node cluster 14 of FIG. 1that is configured by configurator 22 is illustrated according to oneembodiment. Node 16 includes at least one processor 40 that is operativeto execute software or firmware stored in memory 42. Memory 42 includesone or more physical memory locations and may be internal or external toprocessor 40.

FIG. 2 illustrates the software (or firmware) code that is loaded ontoeach node 16, including an operating system 44, a kernel-modemeasurement agent 46, a network topology driver 48, a user-modemeasurement agent 50, a web application server 52, a workload containermodule 54, a service oriented architecture runtime agent 56, and asynthetic workload engine 58. In the illustrated embodiment, kernel-modemeasurement agent 46 and network topology driver 48 require privilegefrom operating system 44 to access certain data, such as data frominput/output (I/O) devices of node 16, for example. Similarly, user-modemeasurement agent 50, web application server 52, workload containermodule 54, service oriented architecture runtime agent 56, and syntheticworkload engine 58 illustratively do not require privilege fromoperating system 44 to access data or to perform their respectivefunctions.

Operating system 44 manages the overall operation of node 16, including,for example, managing applications, privileges, and hardware resourcesand allocating processor time and memory usage. Network topology driver48 is operative to control the network characteristics and parameters ofnode 16 on communication network 18 (FIG. 1). In one embodiment, networktopology driver 48 is operative to change network characteristicsassociated with node 16 based on a configuration file 28 (FIG. 1)received from configurator 22 (FIG. 1).

A network software stack (not shown) is also stored and executed at eachnode 16 and includes a network socket for facilitating communication onnetwork 18 of FIG. 1. In the embodiment described herein, the networksocket includes a TCP socket that is assigned an address and portnumber(s) for network communication. In one embodiment, the networksoftware stack utilizes a network driver of the operating system 44.

Kernel-mode measurement agent 46 and user-mode measurement agent 50 areeach operative to collect and analyze data for monitoring operations andworkload performance at node 16. Kernel-mode measurement agent 46monitors, for example, the number of processor instructions, processorutilization, the number of bytes sent and received for each I/Ooperation, as well as other suitable data or combinations thereof. Anexemplary kernel-mode measurement agent 46 includes SystemTap software.User-mode measurement agent 50 collects performance data not requiringsystem privileges from the operating system 44 for access to the data.An example of this performance data includes application-specific logsindicating the start time and completion time of individual sub-tasks,the rate at which such tasks are executed, the amount of virtual memoryutilized by the system, the amount of input records processed for atask, etc. In one embodiment, agents 46, 50 and/or other monitoringtools are pre-installed on each node 16 and are configured byconfigurator 22 at each node 16 based on configuration files 28 (FIG.1). Alternatively, configurator 22 loads configured agents 46, 50 and/orother monitoring tools onto nodes 16 during workload deployment.

Web application server 52 is an application that controls communicationbetween the node 16 and both control server 12 of FIG. 1 and other nodes16 of node cluster 14. Web application server 52 effects file transferbetween nodes 16 and between control server 12 and nodes 16. Anexemplary web application server 52 is Apache Tomcat.

Workload container module 54 is also stored in memory 42 of each node16. As described herein, control server 12 provides workload containermodule 54 to node 16 based on a user's selection and configuration ofthe workload container module 54. An exemplary workload container module54 includes Apache Hadoop, Memcached, Apache Cassandra, or a customworkload container module provided by a user that is not commerciallyavailable. In one embodiment, workload container module 54 includes afile system 55 comprising a code module that when executed by aprocessor manages data storage in memory 42 and the communication ofdata between nodes 16. An exemplary file system 55 is the distributedfile system (HDFS) of the Apache Hadoop workload container. File system55 supports data replication by storing multiple copies of the data andfiles in node memory 42.

Other suitable workload container modules may be provided, such as theoptional service-oriented architecture (SOA) runtime agent 56 and theoptional synthetic workload engine 58. SOA runtime agent 56 is anothertype of workload container module that when executed by a processor isoperative to coordinate execution of a workload. SOA runtime agent 56performs, for example, service functions such as caching and servingfrequently used files (e.g., images, etc.) to accelerate workloadoperation. An exemplary SOA runtime agent 56 includes Google ProtocolBuffers. Synthetic workload engine 58 includes a workload containermodule that when executed by a processor is operative to activate andexecute a synthetic test workload received via configurator 22 (FIG. 1),as described herein. In the illustrated embodiment, synthetic workloadengine 58 is tailored for execution with a synthetic test workloadrather than for a actual, non-test workload.

Referring to FIG. 3, configurator 22 of control server 12 is illustratedaccording to one embodiment. Configurator 22 illustratively includes anauthenticator 70, a node configurator 72, a network configurator 74, aworkload container configurator 76, a workload configurator 78, a batchprocessor 80, a data monitor configurator 82, and a data aggregator 84,each comprising the one or more processors 22 of control server 12executing respective software or firmware code modules stored in memory(e.g., memory 90) accessible by the processor(s) 22 of control server 12to perform the functions described herein. Authenticator 70 includesprocessor(s) 22 executing an authentication code module and is operativeto authenticate user access to configurator 22, as described herein withrespect to FIG. 7. Node configurator 72 includes processor(s) 22executing a node configuration code module and is operative to selectand configure nodes 16 to identify a cluster of nodes 14 having aspecified hardware and operational configuration, as described hereinwith respect to FIGS. 8-10. Network configurator 74 includesprocessor(s) 22 executing a network configuration code module and isoperative to adjust network parameters of communication network 18 ofFIG. 1, such as for testing and performance analysis and/or foradjusting system power consumption, as described herein with respect toFIGS. 11-17. Workload container configurator 76 includes processor(s) 22executing a workload container configuration code module and isoperative to select and to configure a workload container module foroperation on nodes 16, as described herein with respect to FIGS. 18-21.Workload configurator 78 includes processor(s) 22 executing a workloadconfiguration code module and is operative to select and configure aworkload for execution with the selected workload container by nodes 16.Workload configurator 78 illustratively includes a code synthesizer 79that includes processor(s) 22 executing a synthetic test workloadgeneration code module, and the code synthesizer 79 is operative togenerate a synthetic test workload based on user-defined workloadparameters, as described herein with respect to FIGS. 23 and 32-35.Batch processor 80 includes processor(s) 22 executing a batch processorcode module and is operative to initiate batch processing of multipleworkloads wherein multiple workloads are executed in a sequence on nodecluster 14, as described herein with respect to FIG. 25. Data monitorconfigurator 82 includes processor(s) 22 executing a data monitoringconfiguration code module and is operative to configure monitoring toolsthat monitor performance data real-time during execution of the workloadand collect data, as described herein with respect to FIGS. 26-29. Dataaggregator 84 includes processor(s) 22 executing a data aggregation codemodule and is operative to collect and aggregate the performance datafrom each node 16 and to generate logs, statistics, graphs, and otherrepresentations of the data, as described herein with respect to FIGS.30 and 31.

Output from configurator 22 is illustratively stored in memory 90 ofcontrol server 12. Memory 90, which may be internal or external to theprocessor(s) of control server 12, includes one or more physical memorylocations. Memory 90 illustratively stores the configuration files 28,30 of FIG. 1 that are generated by configurator 22. Memory 90 alsostores log files 98 that are generated by nodes 16 and are communicatedto control server 12 following execution of a workload. As illustrated,an image file 92 of the operating system, an image file 94 of theworkload container selected with workload container configurator 76, andan image file 96 of the workload selected or generated with workloadconfigurator 78 are stored in memory 90. In one embodiment, multipleoperating system image files 92 are stored in memory 90 such that a usermay select an operating system via configurator 22 for installation oneach node 16. In one embodiment, a user may upload an operating systemimage file 92 from a remote memory (e.g., memory 34 of computer 20 ofFIG. 1) onto control server 12 for installation on nodes 16. Theworkload container image file 94 is generated with workload containerconfigurator 76 based on a user's selection and configuration of theworkload container module from multiple available workload containermodules. In the embodiment described herein, workload containerconfigurator 76 configures the corresponding workload container imagefile 94 based on user input received via user interface 200 of FIGS.7-30. Similarly, workload configurator 78 generates and configuresworkload image file 96 based on a user's selection of a workload fromone or more available workloads via user interface 200 of control server12. Workload image file 96 includes a predefined, actual workloadselected by workload configurator 78 based on user input or a synthetictest workload generated by workload configurator 78 based on user input.

In one embodiment, memory 90 is accessible by each node 16 of the nodecluster 14, and control server 12 sends a pointer or other identifier toeach node 16 of node cluster 14 that identifies the location in memory90 of each image file 92, 94, 96. Nodes 16 retrieve the respective imagefiles 92, 94, 96 from memory 90 based on the pointers. Alternatively,control server 12 loads image files 92, 94, 96 and the appropriateconfiguration files 28 onto each node 16 or provides the image files 92,94, 96 and configuration files 28 to nodes 16 by any other suitablemechanism.

As described herein, configurator 22 is operative to automaticallyperform the following actions based on user selections and input:allocate the desired resources (e.g., nodes 16); pre-configure the nodes16 (e.g., network topology, memory characteristics); install theworkload container software in each node 16; deploy user-providedworkload software and data to the nodes 16; initiate monitoring tools(e.g., Ganglia, SystemTap) and performance data to be gathered from eachnode 16; provide live status updates to the user during workloadexecution; collect all data requested by the user, including the resultsof the workload and information gathered by monitoring tools; process,summarize, and display performance data requested by the user; andperform other suitable functions. Further, a user may use configurator22 to create and deploy sequences of workloads running sequentially orin parallel, as described herein. A user may execute any or all of theworkloads repeatedly, while making optional adjustments to theconfiguration or input parameters during or between the executions.Configurator 22 is also operative to store data on designated databasenodes 16 of node cluster 14 based on requests by a user.

FIG. 4 illustrates a flow diagram 100 of an exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for configuring a cloudcomputing system. Reference is made to FIGS. 1 and 3 throughout thedescription of FIG. 4. In the illustrated embodiment, configurator 22configures node cluster 14 of FIG. 1 according to the flow diagram 100of FIG. 4 based on a plurality of user selections received via a userinterface, such as user interface 200 illustrated in FIGS. 7-30. Atblock 102, node configurator 72 of configurator 22 selects a cluster ofnodes 14 from a plurality of available nodes 16. Each node 16 of thecluster of nodes 14 includes at least one processing device 40 andmemory 42 (FIG. 2) and is operative to share processing of a workloadwith other nodes 16 of the cluster 14, as described herein. In theillustrated embodiment, multiple nodes 16 are available for selection byconfigurator 22, and configurator 22 selects a subset of the availablenodes 16 as the node cluster 14. In one embodiment, configurator 22selects at least one type of data to be collected from each node 16 ofthe cluster of nodes 14 based on a user selection received via the userinterface, and data aggregator 84 of configurator 22 collects andaggregates the at least one type of data from each node 16 of thecluster of nodes 14, as described herein with respect to FIGS. 26-30.

At block 104, workload container configurator 76 of configurator 22selects a workload container module for operation on each node 16 of theselected cluster of nodes 14. The workload container module includes aselectable code module that when executed by node 16 is operative toinitiate and coordinate execution of a workload. In one embodiment, theworkload container module is selected from a plurality of availableworkload container modules, as described herein with respect to FIG. 18.In one embodiment, configurator 22 modifies at least one operationalparameter of the workload container module on each node 16 based on userinput received via the user interface. The at least one operationalparameter is associated with at least one of a read/write operation, afile system operation, a network socket operation, and a sortingoperation, as described herein.

In one embodiment, the selected workload container module is a customworkload container module stored on memory remote from cloud computingsystem 10 (e.g., memory 34 of FIG. 1), and configurator 22 loads thecustom workload container module stored on the remote memory onto eachnode 16 of the cluster of nodes 14. For example, a custom workloadcontainer module includes a workload container module that is providedby a user and is not commercially available. In one embodiment, thecustom workload container module includes a configuration file thatcontains user-defined instructions and parameters for executing theworkload. Exemplary instructions include instructions for testingworkload parameters that are uncommon in typical workloads and/or areunique to a specific workload. Other exemplary instructions of a customworkload container module include instructions to redirect the output orlog files of the execution to a different location for further analysis.Alternatively, the workload container module includes a commerciallyavailable, third party workload container module, such as Apache Hadoop,Memcached, Apache Cassandra, etc., that is stored at computing system 10(e.g., memory 90 of FIG. 3) and is available for selection anddeployment by configurator 22.

At block 106, workload configurator 78 of configurator 22 selects aworkload for execution with the workload container module on the clusterof nodes 14. The processing of the selected workload is distributedacross the cluster of nodes 14, as described herein. In one embodiment,the workload is selected from at least one of an actual workload and asynthetic test workload. One or more actual, pre-compiled workloads arestored in a memory (e.g., memory 34 of FIG. 1) accessible by theprocessor of control server 12, and configurator 22 loads a selectedactual workload onto nodes 16. A synthetic test workload is generated byconfigurator 22 based on user-defined workload parameters received viauser interface 200 and is loaded onto nodes 16, as described herein withrespect to FIGS. 23 and 32-35. In one embodiment, configurator 22adjusts, based on a user input received via user interface 200, at leastone communication network parameter to modify or limit the performanceof communication network 18 during execution of the selected workload,as described herein with respect to FIGS. 11-17.

In the illustrated embodiment, configurator 22 provides the userinterface 200 (FIGS. 7-30) that includes selectable node data (e.g.table 258 of FIG. 8), selectable workload container data (e.g.,selectable input 352 of FIG. 18), and selectable workload data (e.g.,selectable input 418 of FIG. 22). The cluster of nodes 14 is selectedbased on a user selection of the selectable node data, the workloadcontainer module is selected based on a user selection of the selectableworkload container data, and the workload is selected based on a userselection of the selectable workload data.

FIG. 5 illustrates a flow diagram 120 of another exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for configuring cloudcomputing system 10. Reference is made to FIGS. 1 and 3 throughout thedescription of FIG. 5. At block 122, workload container configurator 76selects, based on a user selection received via a user interface (e.g.,user interface 200), a workload container module from a plurality ofavailable workload container modules for operation on each node 16 of acluster of nodes 14 of the cloud computing system 10. In the illustratedembodiment, the workload container module is selected based onselectable workload container data, such as inputs 352, 360, 362 of FIG.18 and inputs 352, 401 of FIG. 21, for example. The selected workloadcontainer module includes a selectable code module (e.g., selectablewith inputs 360, 362 of FIG. 18 and input 401 of FIG. 21) operative tocoordinate execution of a workload. In one embodiment, the plurality ofavailable workload container modules includes a custom workloadcontainer module, as described herein. At block 124, node configurator72 configures each node 16 of the cluster of nodes 14 with the selectedworkload container module for executing the workload such thatprocessing of the workload is distributed across the cluster of nodes.As described herein, each node 16 includes a processing device 40 andmemory 42 and is operative to share processing of the workload withother nodes 16 of the cluster of nodes 14. Configurator 22 installs theselected workload container module on each node 16 of the cluster ofnodes 14 and initiates the execution of the workload with the selectedworkload container module on the cluster of nodes 14.

FIG. 6 illustrates a flow diagram 140 of another exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for configuring cloudcomputing system 10. Reference is made to FIGS. 1 and 3 throughout thedescription of FIG. 6. At block 142, node configurator 72 ofconfigurator 22 selects a cluster of nodes 14 from a plurality ofavailable nodes 16 for a cloud computing system 10 that are operative toshare processing of a workload. In the illustrated embodiment, thecluster of nodes 14 is selected based on selectable node data, asdescribed herein.

At block 144, workload container configurator 76 modifies an operationalparameter of a same workload container module of each node 16 based onuser input received via a user interface (e.g., selectable inputs 367and fields 374, 378, 380 of user interface 200 of FIG. 19). The sameworkload container module includes a selectable code module that whenexecuted by the node 16 is operative to coordinate execution of aworkload based on the operational parameter. The operational parameteris associated with at least one of a read/write operation, a file systemoperation, a network socket operation, and a sorting operation, asdescribed herein with respect to FIGS. 19 and 20. Configurator 22modifies the operating parameter(s) prior to deploying the workloadcontainer module onto each node 16, or after deployment of the workloadcontainer module to each node 16 when updating configurations. Theworkload container module when executed by each node 16 is operative tocoordinate execution of the workload on the cluster of nodes 14 based onthe modified operational parameter. In one embodiment, the operationalparameter includes a memory buffer size for a read/write operation, asize of a data block transferred during a read/write operation, a numberof data blocks stored in the memory 42 of each node 16, a number ofprocessing threads of each node 16 allocated for processing requests forthe file system 55, and/or a number of data streams to merge whensorting data. Other suitable operational parameters may be modified, asdescribed with respect to FIGS. 19 and 20.

An exemplary user interface 200 is illustrated in FIGS. 7-30 thatprovides user access to control server 12 of FIG. 3. User interface 200is illustratively a web-based, graphical user interface 200 thatincludes multiple selectable screens configured for display on adisplay, such as on display 21 of computer 20 (FIG. 1). Other suitableuser interfaces may be provided, such as a native user interfaceapplication, a command line driven interface, a programmable API, oranother other type or combination of interfaces. User interface 200includes selectable data, such as selectable inputs, fields, modules,tabs, drop-down menus, boxes, and other suitable selectable data, thatare linked to and provide input to the components 70-84 of configurator22. In one embodiment, the selectable data of user interface 200 isrendered in a manner that allows it to be individually selectable. Forexample, the selectable data is selected by a user with a mouse pointer,by touching a touchscreen of user interface 200, by pressing keys of akeyboard, or by any other suitable selection mechanism. Selected datamay result in the data being highlighted or checked, for example, and anew screen, menu, or pop-up window may appear based on selection of someselectable data (e.g., modules, drop-down menus, etc.).

Reference is made to FIGS. 1-3 throughout the description of userinterface 200. As illustrated in FIG. 7, user interface 200 includesseveral selectable modules that, when selected, provide access toconfigurator 22, thereby allowing user selections and other user inputto configurator 22. In particular, the Authentication and SettingsLibrary module 202 comprises data representing and linked toauthenticator 70 of configurator 22. Instances module 204 comprises datarepresenting and linked to node configurator 72 of configurator 22.Network Configuration module 206 comprises data representing and linkedto network configurator 74 of configurator 22. Workload ContainerConfiguration module 208 comprises data representing and linked toworkload container configurator 76 of configurator 22. WorkloadConfiguration module 210 comprises data representing and linked toworkload configurator 78 of configurator 22. Batch Processing module 212comprises data representing and linked to batch processor 80 ofconfigurator 22. Monitoring module 214 comprises data representing andlinked to data monitor configurator 82 of configurator 22. Control andStatus module 216 comprises data representing and linked to dataaggregator 84 of configurator 22. Components 70-84 of configurator 22implement their respective functions based on the user selections, data,and other user input provided via modules 202-216 of user interface 200.

Referring to FIG. 7, the Authentication and Settings Library module 202is selected. Based on user input to module 202, authenticator 70authenticates user access to configurator 22 as well as loads previouslysaved system configurations. Authenticator 70 grants a user access toconfigurator 22 by confirming credential data entered in the form of anaccess key, a secret key, and/or an EC2 key pair in respective fields220, 222, 224. In the illustrated embodiment, the EC2 key pair of field224 provides root or initial access to newly selected nodes 16 whenusing module 202 to access the Amazon Web Services cloud platform.Authenticator 70 loads a previously saved system configuration from asystem configuration file (e.g., stored on user's computer 20 or controlserver 12 of FIG. 1) based on user selection of input 238. The systemconfiguration file includes workload and workload containerconfigurations, node 16 and network settings information, datamonitoring/collection settings for cloud computing system 10, and allother configuration information associated with a system configurationpreviously saved with configurator 22. Loading a previously saved systemconfiguration file updates configurator 22 with the configurationinformation from the system configuration file. The system configurationfile illustratively includes a JSON file format, although other suitableformats may be provided. After loading the system configuration file,the loaded system configuration may be modified via the modules of userinterface 200. Selection of input 240 causes authenticator 70 to save acurrent system configuration of configurator 22 to a file. Theauthentication data may be included in the saved system configurationfile based on selection of selection box 242.

While the system configuration file is identified and loaded ontocontrol server 12 via a web-based user interface 200, other suitableremote method invocation (RMI) mechanisms may be used to obtain thesystem configuration file. For example, an Apache Hypertext TransferProtocol (HTTP) server, an Apache Tomcat server, a Tomcat servlet usingthe RMI mechanism to pass the system configuration file, or a customapplication (e.g., command line utility) that uses the RMI mechanism topass the system configuration file directly to control server 12.

A settings library 226 provides a table or list of previously createdsystem configuration files that are available for selection andexecution via selectable inputs 227. The selection of input 228 causesauthenticator 70 to update the modules 202-216 with configurationinformation from the system configuration file selected in library 226.A current system configuration (e.g., configured via modules 202-216) issaved to a file and added to library 226 based on selection of input230, and a system configuration file is deleted from library 226 basedon selection of input 234. Selection of inputs 232 and 236 causesauthenticator 70 to upload a system configuration file from a localcomputer (e.g., computer 20 of FIG. 1) to library 226 or to download asystem configuration file from a remote computer (e.g., via theInternet) to library 226, respectively. Library 226 allows one or morepreviously used system configuration to be loaded and executed quickly.The system configuration files of library 226 may be selected andexecuted separately, in parallel, or in a sequence on cloud computingsystem 10. For example, multiple system configuration files may beprovided in library 226 for execution in a batch sequence, whereinconfigurator 22 automatically deploys each selected system configurationin sequence to execute the workload(s) with each system configuration.In the illustrated embodiment, the system configuration is deployed tonodes 16 via the Control and Status module 216 of FIG. 30, as describedherein. The deployment of the system configuration involves configurator22 configuring the cloud computing system 10 with the settings,software, and workload information associated with the systemconfiguration file, as described herein with reference to FIG. 30. Asdescribed herein, configurator 22 illustratively generates one or moreconfiguration files 28 that are routed to each node 16 for configuringthe respective nodes 16. The configuration files 28 deployed to nodes 16include all configuration information contained in the systemconfiguration file loaded via module 202 plus any additionalconfiguration changes made via modules 202-216 after loading the systemconfiguration file.

Referring to FIG. 8, the Instances module 204 is selected forconfiguring the number and characteristics of nodes 16. Based on userinput to module 204, node configurator 72 identifies and selects acluster of nodes 14 having a specified hardware and operationalconfiguration. Instances module 204 includes an Instances tab 250, anInstance Types tab 252, and an Other Instance Settings tab 254. Underthe Instances tab 250 selected in FIG. 8, the number of desired nodes 16for inclusion in node cluster 14 is entered in field 256. Nodeconfigurator 72 generates a default list of nodes 16, each having aspecific hardware configuration, in table 258 upon user selection of thedesired number of nodes 16 with field 256. Table 258 provides a list anda configuration description of the cluster of nodes 14 of FIG. 1. Table258 includes several descriptive fields for each node 16, including thenode number and name, the instance (node) type, the memory capacity, thenumber of core processors (e.g., CPU's), the storage capacity, thequota, the receive/transmit quota, and the receive/transmit cap. Theinstance type generally describes the relative size and compute power ofthe node, illustratively selected from micro, small, medium, large,x-large, 2×-large, 4×-large, etc (see FIG. 9). In the exemplary table258 of FIG. 8, each node 16 is a large type with a memory capacity of7680 megabytes (MB), a storage capacity of 850 MB, and four coreprocessors. Node configurator 72 selects nodes 16 based on the userselection of selectable node data, illustratively selection boxes 259and selectable inputs 262. The type of each node 16 is changeable basedon selection of a node 16 of table 258 (e.g., using inputs 262 or bychecking the corresponding selection boxes 259) and selecting the editinstance type input 260, which causes Instance Types tab 252 to bedisplayed for the selected node 16. Referring to FIG. 9, table 264comprises a list of the types of nodes 16 that are available forselection (i.e., the available server hardware) for use in the nodecluster 14. One or more nodes 16 of table 264 are selected withselectable inputs 265 for replacing the node 16 selected in table 258 ofFIG. 8. In one embodiment, the fields of table 264 (e.g., Memory, VCPUs,Storage, etc.) are modifiable by a user to further identify desiredhardware performance characteristics of the selected nodes 16. Fewer oradditional types of nodes 16 may be available for selection in table264, depending on available server hardware. In the illustratedembodiment, multiple nodes 16 are available for each node type listed intable 264 for adding to node cluster 14.

Referring to FIG. 10, node configurator 72 adjusts the boot-timeconfiguration of each node 16 based on user input provided in theInstance Settings tab 254 of user interface 200. The boot-timeconfiguration includes one or more boot-time parameters that are appliedto individual nodes 16 or groups of nodes 16, or to the entire nodecluster 14. Boot-time parameters such as the computing capacity, systemmemory capacity, and/or storage capacity of each node 16 are limited orconstrained by node configurator 72 based on user inputs to fields 268,270, 272, 274 such that the respective node 16 operates at less than amaximum capacity. The default boot-time parameters are selected based onuser selection of inputs 269, and customized boot-time parameters areselected based on user selection of inputs 271. In the illustratedembodiment, the maximum setting of each adjustable parameter is thedefault, but a user may adjust each parameter upon selecting the“Custom” option with input 271 and entering a configuration setting intothe respective field 268, 270, 272, 274.

In the illustrated embodiment, the number of processing cores of a node16 is adjustable with field 268. For example, if the node 16 selected intable 258 of Instances tab 250 (FIG. 8) has four processing cores, thenumber of processing cores that are enabled during workload executionmay be reduced to zero, one, two, or three cores via field 268, thereby“hiding” one or more processing cores of the selected node 16 from theoperating system 44 (FIG. 2) during workload execution. The visiblesystem memory size is adjustable based on inputs to fields 270, 272,i.e., the system memory that is accessible by operating system 44 (FIG.2). For example, if the node 16 selected in table 258 of Instances tab250 (FIG. 8) has a memory capacity of 2048 MB, the “visible” memory 9(e.g., random access memory) enabled during workload execution may bereduced to less than 2048 MB, thereby “hiding” a portion of the memoryfrom the operating system 44 (FIG. 2) during workload execution.Additional workload arguments or instructions are applied with field 274to adjust additional boot-time parameters. The number of arguments ofthe workload may be increased or decreased based on a number enteredinto field 274. For example, a subset of the instructions of theworkload are selectable for execution with field 274, thereby hiding theremaining instructions from operating system 44 (FIG. 2). Further, anode 16 having a 64-bit architecture is configurable based on input tofield 274 such that it operates in a 32-bit mode wherein only 32 bitsare visible to operating system 44. Additional boot-time parameters maybe entered in field 276. In one embodiment, instructions or code aremanually entered in field 276 by a user to provide additional cloudconfiguration settings. For example, the master node 16 for a map-reduceworkload may be specified via field 276 such that a specific node 16operates as master upon booting. In one embodiment, limiting theoperation of one or more nodes 16 with node configurator 72 is used totest performance of cloud computing system 10, as described herein. Inthe illustrated embodiment, the boot-time configuration settingsspecified in FIG. 10 are provided in a boot-time configuration file 28(FIG. 3) that is provided by node configurator 72 to each node 16 foradjusting the boot-time configuration of the respective nodes 16, asdescribed herein with respect to FIGS. 36-38.

Configurator 22 generates the exemplary Network Settings Wizard window280 illustrated in FIGS. 11-17 based on the user selection of theNetwork Configuration module 206 of FIG. 7. Referring to FIG. 11,Network Settings Wizard 280 provides multiple global network settingstabs each including selectable data for adjusting network parameters ofone or more nodes 16. The adjustable network parameters include networkdelay via tab 282, packet loss via tab 284, packet duplication via tab286, packet corruption via tab 288, packet reordering via tab 290,packet rate control via tab 292, and other custom commands via tab 294.Based on user selections and input via Network Settings Wizard 280 ofuser interface 200, network configurator 74 of FIG. 3 is operative toadjust network parameters of nodes 16 of communication network 18 ofFIG. 1, as described herein. In one embodiment, the modification ofnetwork parameters is used for network testing and performance analysisand/or for adjusting system power consumption. In the illustratedembodiment, network configurator 74 artificially shapes network trafficand behavior based on user input to Network Settings Wizard 280, therebymodeling various types of network topologies. For example, differentcommunication networks have different latencies, bandwidth, performance,etc., depending on network configuration. As such, network configurator74 allows networks with different configurations to be implemented withthe workload execution to test and analyze performance of the differentnetworks with the selected workload. In one embodiment, the testing andanalysis is done in conjunction with batch processor 80 initiatingworkload executions with differing network configurations. For example,an optimal network topology may be determined for execution of aparticular workload with the selected hardware (node 16) configuration.In one embodiment, network configurator 74 is operative to apply networksettings to certain groups or subsets of nodes 16 of node cluster 14.

Referring still to FIG. 11, selectable data associated with implementinga communication network delay is illustrated in tab 282. Networkconfigurator 74 selects and modifies a network delay based on the userselection of inputs (illustratively boxes) 298-301 and fields 302, 304,306, 308, 310, 312. A communication delay for each packet communication(i.e., packets carrying data or information between nodes 16 or betweena node 16 and control server 12) over communication network 18 (FIG. 1)is implemented based on the selection of input 298 and the delay valueentered via fields 302. A variation of the specified communication delayis implemented based on selection of input 299 and variation valueentered via field 304 (e.g., illustratively a variation of plus or minus10 milliseconds). Fields 310, 312 include drop-down menus for selectinga unit of time (e.g., milliseconds, microseconds, etc.) associated withthe respective value of fields 302, 304. A correlation between specifiedcommunication delays is implemented based on selection of input 300 anda correlation value entered via field 306, illustratively a percentagecorrelation value. A distribution of the specified communication delayis implemented based on selection of drop-down menu 301. Thedistribution includes a normal distribution or other suitabledistribution type.

Referring to FIG. 12, selectable data associated with implementing anetwork packet loss rate is illustrated in tab 284. Network configurator74 selects and modifies a packet loss rate (i.e., the rate at whichpackets are artificially lost) based on the user selection of inputs(illustratively boxes) 313, 314 and fields 315, 316. A packet loss rateis implemented for packet communication over network 18 based onselection of input 313 and a rate value entered via field 315. Thepacket loss rate is illustratively entered as a percentage, e.g., 0.1%results in one packet lost every 1000 packets sent by the node 16. Acorrelation for the packet loss rate is implemented based on selectionof input 314 and a correlation value entered via field 316(illustratively a percentage value).

Referring to FIG. 13, selectable data associated with implementing anetwork packet duplication rate is illustrated in tab 286. Networkconfigurator 74 selects and modifies a packet duplication rate (i.e.,the rate at which packets are artificially duplicated) based on the userselection of inputs (illustratively boxes) 317, 318 and fields 319, 320.A packet duplication rate is implemented for packet communication overnetwork 18 based on selection of input 317 and a rate value entered viafield 319. The packet duplication rate is illustratively entered as apercentage, e.g., 0.1% results in one packet duplicated for every 1000packets sent by the node 16. A correlation for the packet duplicationrate is implemented based on selection of input 318 and a correlationvalue entered via field 320 (illustratively a percentage value).

Referring to FIG. 14, selectable data associated with implementing anetwork packet corruption rate is illustrated in tab 288. Networkconfigurator 74 selects and modifies a packet corruption rate (i.e., therate at which packets are artificially corrupted) based on the userselection of input (illustratively box) 321 and field 322. A packetcorruption rate is implemented for packet communication over network 18based on selection of input 321 and a rate value entered via field 322.The packet corruption rate is illustratively entered as a percentage,e.g., 0.1% results in one packet corrupted for every 1000 packets sentby the node 16. In one embodiment, a correlation for the packetcorruption rate may also be selected and implemented.

Referring to FIG. 15, selectable data associated with implementing anetwork packet reordering rate is illustrated in tab 290. Networkconfigurator 74 selects and modifies a packet reordering rate (i.e., therate at which packets are placed out of order during packetcommunication) based on the user selection of inputs (illustrativelyboxes) 323, 324 and fields 325, 326. A packet reordering rate isimplemented for packet communication over network 18 based on selectionof input 323 and a rate value entered via field 325. The packetreordering rate is illustratively entered as a percentage, e.g., 0.1%results in one packet reordered for every 1000 packets sent by the node16. A correlation for the packet reordering rate is implemented based onselection of input 324 and a correlation value entered via field 326(illustratively a percentage value).

Referring to FIG. 16, selectable data associated with implementing anetwork communication rate is illustrated in tab 292. Networkconfigurator 74 selects and modifies a packet communication rate (i.e.,the rate at which packets are communicated between nodes 16) based onthe user selection of inputs (illustratively boxes) 327-330 and fields331-338. A packet communication rate is implemented for communicationnetwork 18 based on selection of input 327 and a rate value entered viafield 331, and a ceiling (maximum) for the packet communication rate isimplemented based on selection of input 328 and a ceiling value enteredvia field 332. A packet burst is implemented based on selection of input329 and a packet burst value entered via field 333, and a ceiling(maximum) for the packet burst is implemented based on selection ofinput 330 and a ceiling value entered via field 334. Fields 335 and 336provide drop-down menus for selecting rate units (illustrativelykilobytes per second), and fields 337 and 338 provide drop-down menusfor selecting burst units (illustratively in bytes).

Referring to FIG. 17, selectable data associated with implementing anetwork communication rate is illustrated in tab 292. Networkconfigurator 74 provides custom commands for modifying networkparameters associated with one or more nodes 16 on communication network18 based on the user selection of input (illustratively box) 340 andcustom commands entered via field 342.

Referring to FIG. 18, the Workload Container Configuration module 208 isselected. Based on user input to module 208 (e.g., the user selection ofselectable workload container data, such as inputs 352, 360, 362),workload container configurator 76 is operative to select and toconfigure a workload container module for operation on the node cluster14. Module 208 includes multiple selectable tabs 350 corresponding tovarious available workload container modules. Each available workloadcontainer module includes a selectable code module that when executed isoperative to initiate and control execution of the workload on nodecluster 14. The workload container modules available via module 208 inthe illustrative embodiment include several third party, commerciallyavailable workload container modules such as Apache Hadoop, Memcached,Cassandra, and Darwin Streaming. Cassandra is an open-source distributeddatabase management system that provides a key-value store for providingbasic database operations. Darwin Streaming is an open-sourceimplementation of a media streaming application, such as QuickTimeprovided by Apple, Inc., that is utilized to stream a variety of moviemedia types. While open-source workload container software isillustratively provided via module 208, closed-source workload containersoftware may also be provided for selection. For example, licenseinformation associated with the closed-source workload containersoftware may be input or purchased via user interface 200. One or morecustom workload container modules may also be loaded and selected viathe “Custom” tab of module 208. Other workload container modules may beprovided. A “Library” tab is also provided that provides access to alibrary of additional workload container modules available forselection, such as previously-used custom workload container modules,for example.

Under the “Hadoop” tab of FIG. 18, workload container configurator 76selects the Apache Hadoop workload container module based on userselection of input 352. The version and build variant of Apache Hadoopis selectable via drop-down menus 360, 362, respectively, under theGeneral tab 354. Operational parameters of the selected workloadcontainer module are adjustable by workload container configurator 76based on user input provided via the Extended tab 356 and the Custom tab358. The operational parameters available for adjustment illustrativelydepend on the selected workload container module. For example, withApache Hadoop selected as the workload container module, Extended tab356 illustrated in FIG. 19 displays a table 366 of exemplary selectableoperational parameters of the Apache Hadoop workload container modulethat are configurable by workload container configurator 76. Workloadcontainer configurator 76 selects the operational parameters forconfiguration based on user selection of corresponding selection boxes367. Table 366 provides several fields for workload containerconfigurator 76 to receive configuration data, including an overridefield 374, a master value field 378, and a slave value field 380. Basedon user selections in the override field 374, the nodes 16 are selectedwhose workload containers are to be adjusted with the correspondingoperational parameter. Nodes 16 are selected in the override field 374based on user selections in the corresponding dropdown menus or based onuser selections of inputs 384. Illustratively, the selection of “never”results in the default configuration of the corresponding operationalparameter being implemented at all nodes 16, the selection of “master”or “slaves” results in the implementation of the parameter adjustment atthe master node 16 or at the slave nodes 16, respectively, and theselection of “always” results in the implementation of the parameteradjustment at all nodes 16 of the node cluster 14. Alternatively,individual nodes 16 of node cluster 14 may be selected forimplementation of the adjusted operational parameter.

In the master value field 378 and slave value field 380, a constraint,data value, or other user selection provides the adjustment value forthe corresponding operational parameter of the workload container in therespective master node 16 or slave nodes 16. A property name field 376illustratively lists the name of the associated operational parameter asreferenced in the code module of the selected workload container module.A description field 382 illustratively displays a general description tothe user of the associated operational parameter. Inputs 386 allow auser to select or to deselect all operational parameters listed in table366. Input 388 allows a user to reverse or “undo” a previous selectionor parameter change, and input 390 allows a user to reset the valuesprovided in fields 374, 378, and 380 to the default settings.

Exemplary operational parameters adjustable with workload containerconfigurator 76 based on user selections in table 366 includeoperational parameters associated with read/write (I/O) operations ofthe node 16, sorting operations, the configuration of the network socketoperation (e.g., TCP socket connection) of the node 16, and the filesystem 55 (e.g., HDFS for Apache Hadoop) of the workload container.Operational parameters associated with read/write operations include,for example, a memory buffer size of the node 16 and a size of a datablock transferred during the read/write operation. The memory buffersize, illustratively shown in row 368 of table 366, corresponds to howmuch data is buffered (temporarily stored in cache) during read/write(I/O) operations of the node 16. In the illustrated embodiment, thememory buffer size is a multiple of a memory page or data block size ofthe node hardware. A memory page or data block, as described herein,refers to a fixed-length block of virtual memory of a node 16 that isthe smallest unit of data used for memory allocation and memorytransfer. In row 368 of FIG. 19, the master and slave node values areillustratively set to 4096 bits, but these values may be adjusted to8192 bits or another suitable multiple of the data block size of thenode processor 40 (FIG. 2). Similarly, the size of the data blocktransferred during read/write operations is also adjustable based onuser input to table 366.

Operational parameters associated with sorting operations include, forexample, the number of data streams to merge simultaneously when sortingdata. Operational parameters associated with the file system (e.g., filesystem 55 of FIG. 2) of the workload container include the number offile system records or files stored in memory 42 of each node 16 (seerow 370, for example) and the number of processing threads of each node16 allocated for processing requests for the file system 55. In theexemplary row 370 of table 366, the number of records stored in memory42 for file system 55 of FIG. 2 is 100000 records for both the masterand slave nodes 16, although other suitable record limits may beentered. In one embodiment, limiting the number of file system recordsserve to limit the replication of files by file system 55.

Operational parameters associated with the configuration and operationof the network socket, such as TCP network socket described herein,involve the interaction of the workload container with the networksocket. For example, the communication delay or latency of the networksocket and the number of packets sent over network 18 (FIG. 1) may beadjusted. For example, row 372 of table 366 allows for theactivation/deactivation via fields 378, 380 of an algorithm,illustratively “Nagle's algorithm” known in the art, to adjust thelatency and number of data packets sent via the TCP socket connection ofthe node 16. Other suitable operational parameters associated with theoperation of network socket may be adjusted.

Another exemplary operational parameter adjustable by workload containerconfigurator 76 includes the number of software tasks executedconcurrently by processor 40 of node 16. For example, a user may specifya number of tasks (e.g., Java tasks) to run concurrently during workloadexecution via input to table 366, and workload container configurator 76adjusts the number of tasks accordingly. Other suitable operationalparameters associated with the workload container may be adjustable.

Referring to Custom tab 358 of FIG. 20, additional configurationadjustments may be implemented for the selected workload containermodule, illustratively the Hadoop workload container module, to allowfor further customization of the selected workload container module.Workload container configurator 76 further adjusts the configuration ofthe selected workload container module based on command strings inputinto fields 392, 394, and 396 and user selection of correspondingselectable boxes 398. In the illustrated embodiment, each of thesefields 392, 394, 396 specify configurations that apply, respectively, tothe Hadoop master node, the Hadoop file system, and the parametersrelated to map-reduce execution, such as number of tasks in a tasktracker, the local directory of where to place temporary data, and othersuitable parameters.

Operational parameters associated with the other available workloadcontainer modules (e.g., Memcached, Cassandra, Darwin Streaming, etc.)are adjusted similarly as described with the Hadoop workload containermodule. Based on the workload container module selected based on input352 and the configuration information provided via tabs 354, 356, 358 ofmodule 208, workload container configurator 76 generates a workloadcontainer image file 94 (FIG. 3) for loading onto nodes 16 of nodecluster 14. In one embodiment, a workload container image file 94 issaved in memory 90 of control server 12 or in memory 42 of nodes 16, andworkload container configurator 76 updates the image file 94 with theconfiguration information. In one embodiment, multiple configurations ofthe workload container module may be saved and then run in a sequence,such as for exploring the impact of the workload container configurationchanges on workload and system performance, for example.

Referring to FIG. 21, workload container configurator 76 selects auser-defined custom workload container module for execution on nodes 16based on user selection of inputs 353, 401 of the “Custom” tab of module208. In the illustrated embodiment, a custom workload container moduleincludes a workload container module that is provided by a user and thatmay not be commercially available, as described herein. Workloadcontainer configurator 76 illustratively loads a compressed zip filethat includes a workload container code module. In particular, the zipfile includes a configuration file or script that contains user-definedparameters for coordinating the execution of a workload on node cluster14. As illustrated in FIG. 21, table 400 provides a list of loadedcustom workload container modules that are stored at control server 12(or at computer 20) and are available for user selection via selectableinput(s) 401. Additional custom workload container modules are uploadedor downloaded and displayed in table 400 based on user selection ofinputs 402, 404, respectively, and a custom workload container module isdeleted from table 400 based on user selection of input 403. A user mayenter the zip folder path and/or configuration script path viarespective fields 406, 408. In one embodiment, the custom workloadcontainer module is stored remote from cloud computing system 10, suchas on memory 34 of computer 20 (FIG. 1), and is uploaded onto memory 90(FIG. 3) of control server 12 based on user selection of input 402.

Referring to FIG. 22, the Workload Configuration module 210 is selected.Based on user input to module 210, workload configurator 78 (FIG. 3) isoperative to select and configure a workload for execution with theselected workload container module by node cluster 14. Workloadconfigurator 78 is also operative to generate a synthetic test workloadbased on user-defined workload parameters that is executed on nodes 16with the selected workload container module. Module 210 includes severalselectable tabs including a workload tab 410, a synthetic kernel tab412, an MC-Blaster tab 414, a settings library tab 416, and a CloudSuitetab 417. Under the workload tab 410 of FIG. 22, the workload to beexecuted is selected by workload configurator 78 based on user selectionof selectable workload data, illustratively including selectable inputs418, 424, and 428. The available workloads illustratively include aworkload adapted for execution on a Hadoop workload container (inputs418), a workload adapted for execution on a Memcached workload container(input 424), or any another suitable workload configured for a selectedworkload container, such as a custom workload (input 428), for example.

Referring to FIG. 22, a Hadoop workload is selected from an actualworkload and a synthetic test workload based on user selection of one ofcorresponding inputs 418. The actual workload, which includes apre-defined code module adapted for the map-reduce functionality of theHadoop workload container, is loaded onto control server 12 based on anidentification of the storage location of the actual workload in field422. In one embodiment, the actual workload is stored on a memory remotefrom cloud computing system 10, such as memory 34 of FIG. 1, and isuploaded to memory 90 of control server 12 via field 422. In anotherembodiment, the actual workload is a sample Hadoop workload that isprovided with the Hadoop workload container module or is anotherworkload pre-loaded onto control server 12. A synthetic test workload isalso selectable based on user selection of corresponding input 418 forexecution on a Hadoop workload container. The number of input records orinstructions to be generated with the synthetic test workload and to beprocessed in the “map” phase of the synthetic test workload may beentered via field 420 and provided as input to synthesizer 79 ofworkload configurator 78 (FIG. 3), as described herein. Other inputparameters for the generation of the synthetic test workload bysynthesizer 79 are configured via the synthetic kernel tab 412, asdescribed herein. While the synthetic test workload is illustrativelyadapted for execution with a Hadoop workload container, synthetic testworkloads may also be selected and generated for other availableworkload containers.

A custom script is loaded as a pre-defined, actual workload forexecution with a selected workload container module via field 430 andupon user selection of input 428. The custom script comprisesuser-provided code that includes one or more execution commands that areexecuted with the selected workload container module by node cluster 14.In the illustrated embodiment, the custom script is used as the workloadexecuted during system testing with batch processor 80, wherein variousnetwork, workload container, and/or other system configuration changesare made during sequential workload executions to monitor the effects onsystem performance, as described herein.

A pre-defined workload may also be loaded for execution with a Memcachedworkload container based on user selection of input 424. In oneembodiment, the Memcached workload includes an in-memory accelerationstructure that stores key-value pairs via “set” commands and retrieveskey-value pairs via “get” commands. A key-value pair is a set of twolinked data items including a key, which is an identifier for an item ofdata, and a value, which is either the data identified with the key or apointer to the location of that data. The Memcached workloadillustratively operates with a selectable MC-Blaster tool whose run timeis selected based on an input value to field 426. MC-Blaster is a toolto stimulate the system under test by generating requests to read/writerecords from Memcached on a number of network (e.g., TCP) socketconnections. Each request specifies a key and a value. The MC-Blastertool is configured via MC-Blaster tab 414 of FIG. 24. Referring to FIG.24, input to field 460 specifies the number of TCP connections toutilize per processing thread, input to field 462 specifies the numberof keys to operate on, and input to fields 464 and 466 specify thenumber of ‘get’ and ‘set’ commands requested to be sent per second,respectively. A user-specified (custom) buffer size may be implementedby workload configurator 78 based on selection of corresponding input469 and a value entered into field 468, and a TCP request may be delayedbased on selection of “on” input 470. A number of processing threads tostart may be customized by workload configurator 78 based on userselection of corresponding input 473 and a value entered in field 472.The default number of processing threads is equal to the number ofactive processing cores of the node 16. The number of UDP replay portsis selected based on input to field 474, and the size (in bytes) of thevalue stored (or returned) resulting from workload execution is selectedbased on input to field 476.

Referring to FIG. 23, a synthetic test workload is generated bysynthesizer 79 based on user input provided via synthetic kernel tab412. In particular, synthesizer 79 of workload configurator 78 (FIG. 3)generates a synthetic test workload based on user-defined parametersprovided in a code module, illustratively a trace file (e.g.,configuration file), that is loaded onto memory 90 of control server 12.The trace file includes data that describe desired computationalcharacteristics of the synthetic test workload, as described herein.Upon user selection of the “synthesize” input 434 of FIG. 23, thelocation of the stored trace file may be identified based on user inputto field 436 or field 438. Field 436 illustratively identifies a harddisk location (e.g., memory 34 of computer 20 of FIG. 1) containing thetrace file, and field 438 illustratively identifies the web address orURL for retrieving the trace file. Table 440 displays the trace filesand previously generated synthetic test workloads that are loaded andavailable for selection. A trace file is loaded and displayed in table440 with user selection of input 442, deleted from table 440 with userselection of input 444, and downloaded (i.e., from the URL identified infield 438) based on user selection of input 446. The trace file isillustratively a JSON file format, although other suitable file typesmay be provided. A maximum number of instructions to be generated in thesynthetic test workload is identified in field 448, and a maximum numberof iterations of the generated synthetic test workload is identified infield 450. Alternatively, a previously generated synthetic test workloadis loaded by workload configurator 78 based on user selection of Libraryinput 432, the identification of the stored location (local hard drive,website, etc.) of the synthetic test workload with field 436 or 438, andthe user selection of the input 441 corresponding to the desiredpre-generated synthetic test workload displayed in table 440. Themaximum number of instructions and iterations of the previouslygenerated synthetic test workload is adjustable with fields 448, 450.

The trace file includes a modifiable data structure, illustratively atable having modifiable fields, that identifies the workloadcharacteristics and user-defined parameters used as input by synthesizer79 for generating the synthetic test workload. The table is displayed ona user interface, such as with user interface 200 or a user interface ofuser computer 20, such that the fields of the table may be modifiedbased on user input and selections to the table. See, for example, table150 of FIG. 32 described herein. The trace file further identifies atleast a portion of a target instruction set architecture (ISA) used asinput by synthesizer 79. The trace file further identifies othercharacteristics associated with instructions of the synthetic workload,including: inter-instruction dependencies (e.g., a first instructiondepends on the completion of a second instruction before executing thefirst instruction), memory register allocation constraints (e.g.,constrain an instruction to take a value from a particular register),and architectural execution constraints (e.g., a limited number of logicunits being available for executing a particular type of instruction).As such, configurator 22 is operative to predict how long workloadinstructions should take to execute based on the executioncharacteristics specified in the trace file.

Exemplary user-defined workload parameters set forth in the trace fileinclude the following: the total number of instructions to be generated;the types of instructions to be generated including, for example, afloating point instruction, an integer instruction, and a branchinstruction; the behavior (e.g., execution flow) of instructionexecution, such as, for example, the probabilities of the execution flowbranching off (i.e., whether branches are likely to be taken duringinstruction execution or whether execution will continue along theexecution flow path and not jump to a branch); the distribution of datadependencies among instructions; the average size of basic blocks thatare executed and/or transferred; and the latencies associated withinstruction execution (i.e., length of time required to execute aninstruction or instruction type, such as how many cycles a particularinstruction or instruction type requires for execution). In oneembodiment, the user-defined workload parameters specify which specificinstructions to use as integer instructions or floating pointinstructions. In one embodiment, the user-defined workload parametersspecify the average number and statistical distribution of eachinstruction type (e.g., integer, floating point, branch). In oneembodiment, each instruction includes one or more input and outputarguments.

In the illustrated embodiment, the workload parameters and instructionset architecture data set forth in the trace file are provided in atable-driven, retargetable manner. Based on changes to the contents ofthe table, configurator 22 is operative to target differentmicroarchitectures and systems as well as different instruction setarchitectures of nodes 16. An exemplary table 150 is illustrated in FIG.32 that includes data representing a set of user-defined workloadparameters to be input to code synthesizer 79. Referring to FIG. 32,table 150 includes an instruction portion 152 that describes acollection of instructions for the generated synthetic test workload andan addressing mode portion 154 that describes addressing modes to beused with the synthetic test workload. Instructions and addressing modesin addition to those illustrated may be provided in the table 150.Instruction portion 152 of table 150 includes several modifiable fields158, 160, 162, 164. Field 158 includes data identifying the instructionto be generated, field 160 includes data identifying the computationtype associated with the instruction, and field 162 includes dataidentifying a mnemonic assigned to assist code generation by thesynthesizer 79. Field 164 includes data identifying the differentaddressing modes (i.e., the way in which the instructions' arguments areobtained from memory).

In the illustrated embodiment, the input command 156(“gen_ops.initialize( )”) indicates that instruction portion 152 of thetable 150 is starting that sets forth the instructions to be generated.Line 166 illustrates one example of user-defined workload parameters forgenerating one or more instructions. Referring to line 166,“D(IntShortLatencyArith)” entered into field 158 specifies an integerarithmetic instruction with short latency, and “op add” and “addq”entered into fields 160, 162 indicate the instruction is an addition or“add” instruction. In one embodiment, short latency indicates that theprocessor (e.g., node processor 40) takes one cycle or a few cycles toexecute the instruction. The “addr_reg0rw_reg1r” of field 164 indicatesthat a first, register 0 argument is “rw” (read and write) and thesecond, register 1 argument is “r” (read). Similarly, the“addr_reg0rw_imm” of field 164 describes another variant of theinstruction in which the first argument (register 0 argument) is “rw”(read and write), and the second argument is an “imm” (“immediate”)value (e.g., a numeral like 123).

Referring to the addressing mode portion 154 of table 150, exemplaryline 170 includes “addr_reg0w_reg1r” of field 172 that identifies aclass of instructions that operate only on registers. The first registerargument (i.e., register 0) is a destination “w” (write) and the secondregister argument (i.e., register 1) is an input “r” (read). The entriesin fields 174 and 176 identify the arguments and indicate “src” for aread argument, “dst” for a write argument, or “rmw” for aread-modify-write argument. In x86 architecture, for example, the firstregister argument may be “rmw” (the argument is read, operated upon, andthen written with the result) or another suitable argument. Additionalor different user-defined workload parameters may be specified via table150.

In one embodiment, the table 150 (e.g., trace file) is generatedoffline, such as with user computer 20, for example, and loaded ontoconfigurator 22. In one embodiment, the table 150 is stored on or loadedonto control server 12 and is displayed with user interface 200 to allowa user to modify the user-defined workload parameters via selectable andmodifiable data displayed by the user interface 200.

Referring to FIG. 33, an exemplary process flow for generating andexecuting a synthetic workload is illustrated. Code synthesizer 79 isillustrated that generates the synthetic test workload and outputs aconfiguration file 28 and a synthetic workload image 96 to each node 16,and synthetic workload engine 58 of each node 16 executes the synthetictest workloads, as described herein. Blocks 60, 62, 64 of FIG. 32provide an abstract representation of the contents provided in the tracefile that is input into synthesizer 79. Block 60 is a general task graphthat represents the execution flow of an instruction set. Block 62represents the task functions that are executed including input, output,begin, and end instructions. Block 64 represents workload behaviorparameters including the data block size, execution duration andlatencies, message propagation, and other user-defined parametersdescribed herein.

Synthesizer 79 illustratively includes a code generator 66 and a codeemitter 68, each comprising the one or more processors 22 of controlserver 12 executing software or firmware code stored on memory (e.g.,memory 90) accessible by processor(s) 22 to perform the functionsdescribed herein. Code generator 66 operates on the data structure(e.g., table) of the trace file describing the user-defined workloadparameters and target instruction set architecture and generates anabstracted synthetic code that has the specified execution properties.Code emitter 68 creates an executable synthetic code (i.e., thesynthetic test workload) from the abstracted synthetic code in a formatsuitable for the execution environment (e.g., assembly code to be linkedin an execution harness, binary code, or position-independent code to belinked with the simulation infrastructure, etc.). In one embodiment, thedesired format of the executable code is hard-coded in synthesizer 79.In another embodiment, the desired format of the executable code isselectable via selectable data of user interface 200. In one embodiment,the executable code is compact in size such that the code may beexecuted via cycle-accurate simulators that are not adapted to executefull-size workloads. Other suitable configurations of synthesizer 79 maybe provided. In one embodiment, synthesizer 79 has access to thecomputer architecture data of the nodes 16 of node cluster 14. As such,synthesizer 79 generates a synthetic test workload targeting specificmicroarchitecture and instruction set architecture based on the knowncomputer architecture data of the node cluster 14. As such, thesynthetic test workload may be targeted to exercise a desired set ofarchitectural characteristics, for example.

The synthetic test workload generated by synthesizer 79 includes a codemodule executable with the selected workload container module on nodes16. When a synthetic test workload is generated and selected forexecution, the synthetic test workload is stored as workload image file96 of FIG. 3 in memory 90 of control server 12. Configurator 22 thenloads the workload image file 96 onto each node 16 for execution, ornodes 16 retrieve the workload image file 96. In one embodiment, withthe Hadoop workload container module selected, the synthetic testworkload is run as the “map” phase of the map-reduce.

In the illustrated embodiment, the synthetic test workload is executedto exercise the hardware of computing system 10 for testing andperformance analysis, as described herein. Synthesizer 79 receivesdesired workload behavior as input via the trace file and produces asynthetic test workload that behaves according to the input. Inparticular, statistical properties of the desired workload behavior arethe input to synthesizer 79, such as the number of instructions to beexecuted and a statistical distribution of the type of instructions, asdescribed herein. For example, a loaded trace file may includeuser-defined parameters that request a program loop that contains 1000instructions, and the trace file may specify that 30% of theinstructions are integer instructions, 10% are branch instructionshaving a particular branch structure, 40% are floating-pointinstructions, etc. The trace file (or field 450 of FIG. 23) may specifythat the loop is to be executed 100 times. Synthesizer 79 then producesthe program loop containing the requested parameters as the synthetictest workload.

In one embodiment, the generated synthetic test workload serves toemulate the behavior of an actual workload, such as a specificproprietary code or complex code of a known application or program. Forexample, some proprietary code contains instructions that are notaccessible or available to a user. Similarly, some complex code containsinstructions that are complicated and numerous. In some instances,creating a workload based on such proprietary or complex code may beundesirable or difficult. As such, rather than creating a workload codemodule that contains all the instructions of the proprietary or complexcode, monitoring tools (offline from configurator 22, for example) areused to monitor how the proprietary or complex code exercises serverhardware (nodes 16 or other server hardware) during execution of theproprietary or complex code. The statistical data gathered by themonitoring tools during the execution of the proprietary code are usedto identify parameters that represent the desired executioncharacteristics of the proprietary or complex code. The collection ofparameters is provided in the trace file. The trace file is then loadedas the input to synthesizer 79, and synthesizer 79 generates syntheticcode that behaves similar to the proprietary code based on thestatistical input and other desired parameters. As such, the complex orproprietary instructions of a particular code are not required to modelbehavior of that code on cloud computing system 10.

In one embodiment, synthesizer 79 operates in conjunction with batchprocessor 80 to execute multiple synthetic test workloads generated bysynthesizer 79 from varying trace files. In one embodiment, synthetictest workloads are generated based on modified user-defined workloadparameters of a table (e.g., table 150 of FIG. 32) that test differenttarget processors, both CPU and GPU's, of the nodes 16.

FIG. 34 illustrates a flow diagram 600 of an exemplary operationperformed by configurator 22 of control server 12 of FIGS. 1 and 3 forconfiguring cloud computing system 10 with a selected workload.Reference is made to FIGS. 1 and 3 throughout the description of FIG.34. In the illustrated embodiment, configurator 22 configures nodecluster 14 of FIG. 1 according to the flow diagram 600 of FIG. 34 basedon a plurality of user selections received via user interface 200. Atblock 602, workload configurator 78 selects, based on a user selection(e.g., selections of inputs 418) received via user interface 200, aworkload for execution on cluster of nodes 14 of the cloud computingsystem 10. The workload is selected at block 602 from a plurality ofavailable workloads including an actual workload and a synthetic testworkload. The actual workload comprises a code module stored in a memory(e.g., memory 90 or memory 34) accessible by the control server 12, asdescribed herein. At block 604, configurator 22 configures cluster ofnodes 14 of cloud computing system 10 to execute the selected workloadsuch that processing of the selected workload is distributed acrosscluster of nodes 14, as described herein.

In one embodiment, configurator 22 provides the user interface 200comprising selectable actual workload data and selectable synthetic testworkload data, and the selection of the workload is based on the userselection of at least one of the selectable actual workload data and theselectable synthetic test workload data. Exemplary selectable actualworkload data includes selectable input 418 of FIG. 22 corresponding tothe “actual workload” and selectable inputs 424, 428 of FIG. 22, andexemplary selectable synthetic test workload data includes selectableinput 418 of FIG. 22 corresponding to the “synthetic workload” andselectable inputs 434, 436, 441 of FIG. 23. In one embodiment, workloadconfigurator 78 selects at least one of a pre-generated synthetic testworkload and a set of user-defined workload parameters based on the userselection of the selectable synthetic test workload data. Thepre-generated synthetic test workload comprises a code module (e.g.,loaded via library input 432) stored in a memory (e.g., memory 90 ormemory 34) accessible by the control server 12. The synthesizer 79 isoperative to generate a synthetic test workload based on the selectionof the set of user-defined workload parameters, illustratively providedvia the trace file described herein. The user-defined workloadparameters of the trace file identify execution characteristics of thesynthetic test workload, as described herein.

As described herein, exemplary user-defined workload parameters includeat least one of: a number of instructions of the synthetic testworkload, a type of instruction of the synthetic test workload, alatency associated with an execution of at least one instruction of thesynthetic test workload, and a maximum number of execution iterations ofthe synthetic test workload, and the type of instruction includes atleast one of an integer instruction, a floating point instruction, and abranch instruction. In one embodiment, an execution of the synthetictest workload by the cluster of nodes 14 is operative to simulateexecution characteristics associated with an execution of an actualworkload by the cluster of nodes 14, such as a complex workload orproprietary workload, as described herein.

FIG. 35 illustrates a flow diagram 610 of an exemplary operationperformed by configurator 22 of control server 12 of FIGS. 1 and 3 forconfiguring cloud computing system 10 with a synthetic test workload.Reference is made to FIGS. 1 and 3 throughout the description of FIG.35. In the illustrated embodiment, configurator 22 configures nodecluster 14 of FIG. 1 according to the flow diagram 610 of FIG. 35 basedon a plurality of user selections received via user interface 200. Atblock 612, code synthesizer 79 of workload configurator 78 generates asynthetic test workload for execution on cluster of nodes 14 based on aset of user-defined workload parameters provided via user interface 200.The set of user-defined workload parameters (e.g., provided with thetrace file) identify execution characteristics of the synthetic testworkload, as described herein. At block 614, configurator 22 configurescluster of nodes 14 with the synthetic test workload to execute thesynthetic test workload such that processing of the synthetic testworkload is distributed across the cluster of nodes, as describedherein.

In one embodiment, the generation of the synthetic test workload isfurther based on computer architecture data that identifies at least oneof an instruction set architecture and a microarchitecture associatedwith cluster of nodes 14. As described herein, in one embodimentconfigurator 22 stores the computer architecture data in memory (e.g.,memory 90) such that configurator 22 can identify the instruction setarchitecture and microarchitecture of each node 16 of cluster of nodes14. As such, configurator 22 generates the synthetic test workload suchthat it is configured for execution with specific computer architectureof the nodes 16 of node cluster 14 based on the computer architecturedata stored in memory. In one embodiment, code synthesizer 79 generatesa plurality of synthetic test workloads each based on a differentcomputer architecture associated with nodes 16 of cluster of nodes 14,and each computer architecture includes at least one of an instructionset architecture and a microarchitecture. In one embodiment,configurator 22 provides the user interface 200 comprising selectablesynthetic test workload data, and workload configurator 78 selects theset of user-defined workload parameters for generation of the synthetictest workload based on user selection of the selectable synthetic testworkload data. Exemplary selectable synthetic test workload dataincludes selectable input 418 of FIG. 22 corresponding to the “syntheticworkload” and selectable inputs 434, 436, 441 of FIG. 23. In oneembodiment, the set of user-defined workload parameters are identifiedin a data structure (e.g., table 150 of FIG. 32) displayed on a userinterface (e.g., user interface 200 or a user interface displayed ondisplay 21 of computer 20), and the data structure includes a pluralityof modifiable input fields each identifying at least one user-definedworkload parameter, as described herein with respect to table 150 ofFIG. 32. In one embodiment, configurator 22 selects a modified hardwareconfiguration of at least one node 16 of node cluster 14 based on a userselection received via user interface 200 (e.g., selection of boot-timeparameters with inputs 269-276). In this embodiment, configurator 22configures cluster of nodes 14 with the synthetic test workload toexecute the synthetic test workload on the cluster of nodes 14 havingthe modified hardware configuration, and the modified hardwareconfiguration results in at least one of a reduced computing capacityand a reduced memory capacity of the at least one node 16, as describedherein.

Referring again to FIG. 23, a previously saved workload may be loadedfrom a local memory (e.g., memory 90 of FIG. 3) via Settings Library tab416. The workload loaded via the Settings Library tab 416 may include anactual workload, a synthetic test workload, a custom script, or anyother workload suitable for execution with a selected workload containermodule. The loaded workload configuration may be modified based on userinputs to module 210 of user interface 200. A current workloadconfiguration may also be saved to memory 90 via the Settings Librarytab 416.

In the illustrated embodiment, a Cloud Suite workload collection mayalso be loaded and configured via tab 417. CloudSuite is a collection ofworkloads that comprise typical cloud workloads that are utilized tocharacterize cloud systems.

Referring to FIG. 25, the Batch Processing module 212 is selected. Basedon user input to module 212, batch processor 80 (FIG. 3) is operative toinitiate batch processing of multiple workloads. Batch processor 80 isalso operative to initiate execution of one or more workloads having aplurality of different configurations, such as different networkconfigurations, different workload container configurations, differentsynthetic workload configurations, and/or different node configurations(e.g., boot-time configurations, etc.) described herein. Based on userinput, batch processor 80 initiates the execution of each workloadand/or configuration in a sequence on node cluster 14 such that manualintervention is not required for all workloads to be run to completion.Further, batch processor 80 may configure one or more workloads may torun multiple times based on user settings received via module 212 ofuser interface 200. Batch processor 80 is operative to execute actualworkloads and/or synthetic test workloads as a batch. In the illustratedembodiment, performance data is monitored and aggregated from the batchprocessing of workloads to enable automatic system tuning, as describedherein with respect to FIGS. 47 and 48, for example.

The number of executions for a batch of workloads and/or configurationsis specified via repeat count field 480. Based on user input to field480, batch processor 80 executes one or more workloads the specifiednumber of iterations. A batch sequence table 482 comprises display datalisting the batch jobs to be executed by the node cluster 14. A batchjob includes one or more workloads that are adapted for execution aspecified number of times (e.g., as specified based on input to field480). In one embodiment, a batch job includes one or more cloud systemconfigurations that are adapted for execution with one or more workloadsa specified number of times. While only one batch job is listed in table482, multiple batch jobs may be added to the table 482. Batch processor80 selects the listed batch job(s) for execution based on user selectionof input(s) 483 corresponding to the listed batch job(s). In oneembodiment, the selected batch jobs are executed in a sequence in theorder they are listed in table 482. The batch job is illustratively in aJSON file format, although other suitable formats may be used. The batchjobs listed in table 482 are edited, added, and deleted based on userselection of inputs 484, 486, 488, respectively. The order of the batchsequence is adjustable based on user selection of inputs 490, 492 tomove a selected batch job to a different position in the sequencedisplayed in table 482. A batch sequence and other settings associatedwith the execution of the batch job may be loaded from memory (e.g.,memory 34 or memory 90) via selectable input 494, and a currentlyconfigured batch sequence is saved to memory (e.g., memory 34 or memory90) via selectable input 496. Inputs 484-496 are illustrativelyselectable buttons.

Referring to FIG. 26, the Monitoring module 214 is selected. Based onuser input to module 214, data monitor configurator 82 (FIG. 3) isoperative to configure one or more data monitoring tools used formonitoring and collecting performance data during execution of aworkload on the node cluster 14. Data monitor configurator 82 isoperative to configure monitoring tools that monitor data related to theperformance of node 16, the workload, the workload container, and/ornetwork 18. In one embodiment, the monitoring tools configured by datamonitor configurator 82 include both commercially available monitoringtools and custom monitoring tools provided by a user. The monitoringtools collect data from multiple sources within cloud computing system10 and other available nodes 16. For example, the monitoring toolsinclude kernel-mode measurement agent 46 and user-mode measurement agent50 that collect data at each node 16 (FIG. 2). Control server 12 alsoincludes one or more monitoring tools operative monitor network andcomputing performance on node cluster 14. In one embodiment, based onuser input (e.g., input to fields 530, 532 of FIG. 27), data monitorconfigurator 82 specifies a sampling rate at which the monitoringtool(s) monitors data from nodes 16. Data monitor configurator 82 isoperative to configure and initiate the operation of multiple datamonitoring tools, including an Apache Hadoop monitoring tool provided oneach node 16 (tab 500), a Ganglia tool provided on control server 12(tab 502), a SystemTap tool provided on each node 16 (tab 504), andvirtual memory statistics and I/O statistics monitoring tools providedon one or more nodes 16 (tab 506).

The Hadoop monitoring tool monitors the performance of nodes 16 at theworkload container level when the Hadoop workload container module isselected for execution on nodes 16. The Hadoop monitoring tool is loadedby configurator 22 onto each node 16 with the Hadoop workload containermodule to monitor data associated with the performance of the Hadoopworkload container module based on the monitoring configurationidentified in FIG. 26. As illustrated in FIG. 26, various monitoringparameters associated with the Hadoop monitoring tool are configured bydata monitor configurator 82 based on user input to several modifiablefields and drop-down menus. The modifiable monitoring parameters includea default log level (selected based on input to drop-down menu 508), amaximum file size of collected data (selected based on input to field510), a total size of all files of collected data (selected based oninput to field 512), a log level of the JobTracker tool of the Hadoopworkload container (selected based on input to drop-down menu 514), alog level of the TaskTracker tool of the Hadoop workload container(selected based on input to drop-down menu 516), and a log level of theFSNamesystem tool of the Hadoop workload container (selected based oninput to drop-down menu 518). A log level identifies the type of data tocollect via the Hadoop monitoring tool, such as information (“INFO”),warnings, errors, etc. The JobTracker, Tasktracker, and FSNamesystemtools of the Hadoop workload container include various processes anddata tracked by data monitor configurator 82, including the initiationand completion of a workload at the master node 16, metadata associatedwith file system 55 (FIG. 2), and the initiation of the map and reducetasks at worker nodes 16, for example. Other suitable data may becollected with the Hadoop monitoring tool.

Referring to FIG. 27, the Ganglia monitoring tool is also operative tomonitor and collect performance data of cloud computing system 10 basedon the monitoring configuration implemented by data monitor configurator82. Ganglia is a known system monitoring tool that provides remote liveviewing (e.g., via control server 12) of system performance as well asgraphs and charts showing historical statistics. In the illustratedembodiment, the Ganglia monitoring tool is executed on control server 12based on the configuration data provided with data monitor configurator82. Exemplary data monitored with Ganglia includes processing loadaverages of node processor 40 (CPU's) during workload execution, theutilization (e.g., stall or inactive time, percentage of time spentprocessing, percentage of time spent waiting, etc.) of node processors40 and network 18 during workload execution, and other suitable data.The Ganglia monitoring tool is enabled and disabled by data monitorconfigurator 82 based on user selection of selectable inputs 520, and aunicast or a multicast communication mode is selected by data monitorconfigurator 82 based on user selection of selectable inputs 522. Otherconfigurable monitoring parameters associated with Ganglia include adata refresh interval of a generated graph of collected data (selectedbased on input to field 524), a cleanup threshold (selected based oninput to field 526), and an interval for sending metadata (selectedbased on input to field 528). The data input into fields 524, 526, and528 are illustratively in seconds. Data monitor configurator 82 isoperative to adjust the collection (i.e., sampling) interval and sendingintervals based on values (illustratively in seconds) entered intorespective fields 530, 532 for collecting data during workload executionassociated with the node processor 40 (CPU), the processing load onnodes 16 (e.g., associated with the workload being executed), the usageof node memory 42, the network performance of the nodes 16 on thecommunication network 18, and the hard disk usage of each node 16.

The SystemTap tool is a kernel-mode measurement agent 46 (FIG. 2) thatincludes SystemTap monitoring software operative extract, filter, andsummarize data associated with nodes 16 of cloud computing system 10. Inone embodiment, the SystemTap tool is executed on each node 16.SystemTap is implemented with Linux based operating systems. SystemTapallows a customized monitoring script to be loaded onto each node 16with customized monitoring configurations, including, for example, thesampling rate and the generation and display of histograms. Asillustrated in FIG. 28, with the “Script” tab selected, SystemTap isenabled or disabled by data monitor configurator 82 based on userselection of inputs 536. A SystemTap script file is downloaded tocontrol server 12, added for display in table 538, or removed/deletedfrom display in table 538 by data monitor configurator 82 based on userselection of respective inputs (buttons) 540. Table 538 comprisesdisplay data representing the script files that are available forselection based on user selection of corresponding input(s) 539. Datamonitor configurator 82 loads the selected script file of table 538 ontoeach node 16 upon deployment of the cloud configuration by configurator22. Other suitable configuration options are available based on userinput and selections via tabs 534 for the SystemTap monitoring tool,including configuration of disk I/O, network I/O, and diagnostics, forexample.

Referring to FIG. 29, the I/O Time tab 506 provides user access toconfigure additional monitoring tools, including virtual memorystatistics (VMStat) and input/output statistics (IOStat) that are loadedon one or more nodes 16. VMStat collects data associated withavailability and utilization of system memory and block I/O controlledwith the operating system, the performance of processes, interrupts,paging, etc., for example. For example, VMStat collects data associatedwith a utilization of system memory such as the amount or percent oftime that system memory and/or the memory controller is busy performingread/write operations or is waiting. IOStat collects data associatedwith statistics (e.g., utilization, availability, etc.) of storage I/Ocontrolled with the operating system, for example. For example, IOStatcollects data associated with the percentage of time that processingcores of the processor 40 of the corresponding node 16 is busy executinginstructions or waiting to execute instructions. VMStat and IOStat areenabled/disabled by data monitor configurator 82 based on correspondinguser selection of respective inputs 546, 548, and the sampling rate(i.e., refresh interval) are selected by data monitor configurator 82based on values (illustratively in seconds) entered into fields 550,552. Based on user selection of corresponding “enabled” inputs 546, 548and values input into fields 550, 552 of tab 506, data monitorconfigurator 82 configures the VMStat and IOStat monitoring tools, andconfigurator 22 loads the tools onto each node 16 upon user selection ofthe corresponding “enabled” inputs 546, 548.

The monitoring tools configured with data monitor configurator 82cooperate to provide dynamic instrumentation for cloud computing system10 for monitoring system performance. Based on the data collected viathe configured monitored tools, configurator 22 is operative to diagnosesystem bottlenecks and to determine optimal system configurations (e.g.,hardware and network configurations), for example, as described herein.Further, data monitor configurator 82 provides a common user interfaceby displaying Monitoring module 214 on user interface 200 for receivinguser input used to configure each monitoring tool and for displayingmonitored data from each tool.

Referring to FIG. 30, the Control and Status module 216 is selected thatcomprises selectable data. Based on user input to module 216,configurator 22 is operative to launch (i.e., deploy) the systemconfiguration to node cluster 14 by generating multiple configurationfiles 28 that are loaded onto each node 16. Configurator 22 initiatesdeployment of the current system configuration (i.e., the systemconfiguration currently identified with modules 202-216) based on userselection of selectable input 560. Batch processor 80 of configurator 22initiates the batch processing of one or more workloads and/orconfigurations, i.e., the batch sequence identified in table 482 of FIG.25, based on user selection of selectable input 562. Workloadconfigurator 78 of configurator 22 initiates the execution of customworkloads, such as custom workloads identified in field 430 of FIG. 22,based on user selection of selectable input 564. Upon deployment of thesystem configuration based on user selection of input 560, 562, or 564,configurator 22 automatically configures each selected node 16 with theselected node and network settings, workload, workload container module,data monitoring tools, etc., and instructs node cluster 14 to startexecuting the selected workload and/or batch jobs based on the systemconfiguration information. Configurator 22 terminates or pauses aworkload execution before completion based on user selection ofrespective selectable inputs 566, 568. Configurator 22 restarts aworkload currently executing on node cluster 14 based on user selectionof selectable input 570. Configurator 22 skips a workload currentlyexecuting on node cluster 14 based on user selection of selectable input572 such that, for example, nodes 16 proceed to execute a next workloadof a batch. Based on selection of selectable input 576, data monitorconfigurator 82 of configurator 22 implements the data monitoring tools,settings, and configuration identified via module 214. In oneembodiment, implementing data monitoring settings on nodes 16 comprisesgenerating a corresponding configuration file 28 (FIG. 3) that isprovided to each node 16. Based on user selection of input 574,configurator 22 terminates or shuts down the node cluster 14 followingcompletion of the workload execution(s), i.e., following the receipt ofa result of the workload execution from node cluster 14 and thecollection of all requested data. Inputs 560-572 as well as inputs582-595 are illustratively selectable buttons.

System status is provided during workload execution via displays 578,580. Displays 578, 580 shows the progress of the workload execution aswell as status information associated with each active node 16 of nodecluster 14. The display of the system status is enabled or disabledbased on user selection of button 595.

In the illustrated embodiment, node configurator 72, networkconfigurator 74, workload container configurator 76, workloadconfigurator 78, batch processor 80, and data monitor configurator 82(FIG. 3) each automatically generate at least one correspondingconfiguration file 28 following deployment initiation via input 560,562, or 564 to implement their respective configuration functions. Theconfiguration files 28 contain the corresponding configuration data andinstructions for configuring each node 16 of the node cluster 14, asdescribed herein. In one embodiment, configurator 22 automatically loadseach configuration file 28 onto each node 16 of node cluster 14following the generation of the files 28. Alternatively, a singleconfiguration file 28 is generated that contains the configuration dataand instructions from each component 70-84 of configurator 22, andconfigurator 22 automatically loads the single configuration file 28onto each node 16 of node cluster 14 following generation of theconfiguration file 28. Each image file 92, 94, 96 corresponding to therespective operating system, workload container module, and workload arealso loaded onto each node upon launching the configuration deploymentwith input 560, 562, or 564. Alternatively, nodes 16 may retrieve orrequest the configuration file(s) 28 and/or image files 92, 94, 96following the generation of files 28 and image files 92, 94, 96 byconfigurator 22.

The configuration files 28 deployed to nodes 16, as well as systemconfiguration files saved via input 240 of FIG. 7, include allconfiguration data and information selected and loaded based on userinput to and default settings of modules 202-216. For example, theconfiguration file 28 generated by node configurator 72 includes thenumber of nodes 16 to allocate and/or use for node cluster 14 and thehardware requirements and boot time configuration of each node 16, asdescribed herein. The hardware requirements include RAM size, number ofCPU cores, and available disk space, for example. The configuration file28 generated by network configurator 74 includes, for example, globaldefault settings that apply to all nodes 16; group settings includingwhich nodes 16 belong to a given group of node cluster 14, settings fornetwork traffic within the node group, and settings for network trafficto other node groups of node cluster 14; node-specific settingsincluding custom settings for network traffic between arbitrary nodes16; network parameters including latency, bandwidth, corrupted anddropped packets rates, corrupted and dropped packets correlation anddistribution, and rate of reordered packets, as described herein withrespect to FIGS. 11-17; and other suitable network parameters andnetwork topology configuration data. The configuration file 28 generatedby workload container configurator 76 includes, for example,configuration settings for the primary workload container software usedto run the workload. The configuration file 28 generated by workloadconfigurator 78 includes, for example, configuration settings for theselected pre-defined or synthetic workload to be run on nodes 16. Theconfiguration settings may include synthetic test workload configurationdata including a synthetic test workload image file, a maximuminstructions count, a maximum iterations count, and a ratio of I/Ooperations, for example.

Upon initiation of deployment via input 560 (or inputs 562, 564),configurator 22 automatically performs several operations. According toone illustrative embodiment, configurator 22 allocates and starts thedesired nodes 16 to select the cluster of nodes 14. Configurator 22 thenpasses the address (e.g., IP address) of the control server 12 to eachnode 16 and assigns and passes an identifier and/or address to each node16. In one embodiment, each node 16 is configured to automaticallycontact control server 12 and to request the one or more configurationfiles 28 that describe the job and other configuration informationfollowing receipt of the control server 12 address. Each node 16communicates with control server 12 using any suitable mechanism,including, for example, a specified RMI mechanism (e.g., web-basedinterface) to communicate directly with control server 12, HTTP requeststo interact with control server 12 via Apache HTTP or Tomcat servers, ora remote shell mechanism.

In one embodiment, configurator 22 waits until a request is receivedfrom each node 16 of node cluster 14. In one embodiment, if a node 16fails to start, i.e., based on a lack of request or acknowledgement fromthe node 16, configurator 22 attempts to restart that node 16. If thenode 16 continues to fail to start, configurator 22 identifies andrequests another available node 16 not originally included in nodecluster 14 to take the place of the failed node 16. The replacement node16 includes hardware specifications and processing capabilities that arethe same or similar to the failed node 16. In one embodiment,configurator 22 continues to monitor nodes 16 throughout workloadexecution, and restarts nodes 16 (and the workload) that stopresponding. Configurator 22 may detect nodes 16 not responding duringworkload execution based on failed data monitoring or other failedcommunications.

Upon configurator 22 receiving a request from each node 16 of the nodecluster 14, configurator 22 determines that each node 16 is ready toproceed. In one embodiment, configurator 22 then provides each node 16with the required data, including configuration file(s) 28, theaddresses and ID's of other nodes 16 in node cluster 14, and image files92, 94, 96. Upon receipt of the required data from control server 12,the role of each node 16 in node cluster 14 is determined. In oneembodiment, the role determination is made by control server 12 (e.g.,automatically or based on user input) and communicated to nodes 16.Alternatively, the role determination is made by node cluster 14 using adistributed arbitration mechanism. In one embodiment, the roledetermination is dependent on the workload. For example, for a nodecluster 14 operating with the Hadoop workload container, a first node 16may be designated as the master node 16 (“namenode”) and the remainingnodes 16 may designated as the slave/worker nodes 16 (“datanodes”). Inone embodiment, the role determination of a node 16 further depends onthe hardware properties of the node 16. For example, a group of nodes 16with slower node processors 40 may be designated as database servers forstoring data, and another group of nodes 16 with faster node processors40 may be designated as compute nodes for processing the workload. Inone embodiment, the role determination is based on user input providedvia configuration file 28. For example, a user may assign a firstnode(s) 16 to perform a first task, a second node(s) 16 to perform asecond task, a third node(s) 16 to perform a third task, and so on.

Each node 16 proceeds to configure its virtual network settings based onthe network configuration data received via configuration file(s) 28.This may include, for example, using a network delay and/or a packetloss emulator, as described herein. Each node 16 further proceeds toinstall and/or configure the user-requested software applications,including the workload container code module received via workloadcontainer image file 94. In one embodiment, multiple workload containermodules (e.g., multiple versions/builds) are pre-installed at each node16, and a soft link to the location of the selected workload containermodule is created based on configuration file 28. If a synthetic testworkload is generated and selected at control server 12, each node 16proceeds to activate the synthetic test workload based on workload imagefile 96. Each node 16 further proceeds to run the diagnostic andmonitoring tools (e.g., Ganglia, SystemTap, VMStat, IOStat, etc.) basedon the configuration information. Finally, each node 16 proceeds tostart execution of the selected workload.

In the illustrated embodiment, each step performed by configurator 22and nodes 16 following deployment launch are synchronized across nodes16 of node cluster 14. In one embodiment, configurator 22 of controlserver 12 coordinates nodes 16, although one or more nodes 16 of nodecluster 14 may alternatively manage synchronization. In one embodiment,the synchronization mechanism used for coordinating node operationcauses each node 16 to provide status feedback to control server 12 on aregular basis. As such, nodes 16 failing to report within a specifiedtime are assumed to have crashed and are restarted by configurator 22.Configurator 22 may also provide a status to the user to indicateprogress of the job, such as via displays 578, 580 of FIG. 30.

Upon completion of the job, data aggregator 84 (FIG. 3) is operative tocollect data from each node 16. In particular, the data collected by themonitoring tools of each node 16 (e.g., job output, performancestatistics, application logs, etc.; see module 214) are accessed bycontrol server 12 (e.g., memory 90 of FIG. 3). In one embodiment, dataaggregator 84 retrieves the data from each node 16. In anotherembodiment, each node 16 pushes the data to data aggregator 84. In theillustrated embodiment, the data is communicated to control server 12 inthe form of log files 98 from each node 16, as illustrated in FIG. 31(see also FIG. 3). Each log file 98 includes the data collected by oneor more of the various monitoring tools of each node 16. As describedherein, data aggregator 84 is operative to manipulate and analyze thecollected data from the log files 98 and to display (e.g., via display21 of FIG. 1) the aggregated data to the user in the form of graphs,histograms, charts, etc. Data aggregator 84 also aggregates data frommonitoring tools provided on control server 12, such as the Gangliamonitoring tool described in FIG. 27.

Referring again to FIG. 30, data aggregator 84 is operative to collectand aggregate the performance data from each node 16 and to generatelogs, statistics, graphs, and other representations of the data based onuser selection of corresponding inputs 582-594 of module 216. Dataaggregator 84 gathers raw statistical data provided in the log files 98and provided with other monitoring tools based on user selection ofinput 586. Data aggregator 84 downloads, based on user selection ofinput 588, all log files 98 from nodes 16 to the local file system,where it may be further analyzed, or stored for historical trendanalysis. Data aggregator 84 retrieves only the log files associatedwith the SystemTap monitoring tool based on user selection of input 590.Data aggregator 84 displays one or more of the log files 98 provided bynodes 16 on user interface 200 based on user selection of input 582.Data aggregator 84 displays statistical data on user interface 200 inthe form of graphs and charts based on user selection of input 584. Thestatistical data include performance data associated with, for example,the performance of network 18 and network communication by nodes 16, theperformance of various hardware components of node 16, the workloadexecution, and the performance of the overall node cluster 14. Dataaggregator 84 generates one or more graphs for display on user interface200 illustrating various data collected from nodes 16 and from othermonitoring tools based on user selection of input 592.

In one embodiment, data aggregator 84 selects the data to display basedon the data selected for monitoring with the monitoring tools configuredin Monitoring module 214. In another embodiment, data aggregator 84selects the data aggregated and displayed based on user inputs toControl and Status module 216. For example, a user selects which logfiles 98, statistical data, and graphs to display upon selectingrespective inputs 582, 584, and 592. In one embodiment, data aggregator84 selects which data to display in graphs and selects how to displaythe data (e.g., line graph, bar graph, histogram, etc.) based on userinputs to user interface 200. Exemplary graphical data displayed basedon selection of input 592 include processor speed versus added networkdelay, workload execution speed versus number of processor cores,workload execution speed versus number of processing threads per core,the number of data packets transmitted or received by a particular node16 over time, the number of data packets of a certain size communicatedover time, the time spent by data packets in a network stack, etc.

Configuring Boot-Time Parameters of Nodes of the Cloud Computing System

FIG. 36 illustrates a flow diagram 620 of an exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for configuring aboot-time configuration of cloud computing system 10. Reference is madeto FIGS. 1 and 3 throughout the description of FIG. 36. In theillustrated embodiment, configurator 22 configures node cluster 14 ofFIG. 1 according to the flow diagram 620 of FIG. 36 based on a pluralityof user selections received via user interface 200. At block 622,configurator 22 provides user interface 200 comprising selectableboot-time configuration data. Exemplary selectable boot-timeconfiguration data includes selectable inputs 269, 271 and fields 268,270, 272, 274, 276 of the displayed screen of FIG. 10. At block 624,node configurator 72 of configurator 22 selects, based on at least oneuser selection of the selectable boot-time configuration data, aboot-time configuration for at least one node 16 of a cluster of nodes14 of the cloud computing system 10.

At block 626, configurator 22 configures the at least one node 16 of thecluster of nodes 14 with the selected boot-time configuration to modifyat least one boot-time parameter of the at least one node 16. Forexample, the at least one boot-time parameter includes a number ofprocessing cores (based on input to field 268) of the at least one node16 that are enabled during an execution of the workload and/or an amountof system memory (based on input to fields 270, 272) that is accessibleby the operating system 44 (FIG. 2) of the at least one node 16.Further, a modified boot-time parameter may identify a subset of theplurality of instructions of the workload to be executed by the at leastone node 16 based on the number of instructions input to field 274 andselection of the corresponding custom input 271. As such, the workloadis executed with the cluster of nodes 14 based on the modification ofthe at least one boot-time parameter of the at least one node 16. In oneembodiment, configurator 22 initiates the execution of the workload, andthe cluster of nodes 14 executes the workload with at least one of areduced computing capacity and a reduced memory capacity based on themodification of the at least one boot-time parameter. In particular, amodification to the number of processing cores with field 268 andselection of corresponding input 271 serves to reduce the computingcapacity, and a modification to the number of system memory with fields270, 272 and selection of corresponding input 271 serves to reduce thememory capacity.

In one embodiment, node configurator 72 selects, based on at least oneuser selection of the selectable boot-time configuration data, a firstboot-time configuration for a first node 16 of the cluster of nodes 14and a second boot-time configuration for a second node 16 of the clusterof nodes 14. In this embodiment, the first boot-time configurationincludes a first modification of at least one boot-time parameter of thefirst node 16 and the second boot-time configuration includes a secondmodification of at least one boot-time parameter of the second node 16,and the first modification is different from the second modification. Inone example, the first boot-time configuration includes enabling twoprocessing cores of the first node 16, and the second boot-timeconfiguration includes enabling three processing cores of the secondnode 16. Other suitable modifications of boot-time parameters of eachnode 16 may be provided as described above.

FIG. 37 illustrates a flow diagram 630 of an exemplary operationperformed by a node 16 of the cluster of nodes 14 of FIG. 1 forconfiguring a boot-time configuration of the node 16. Reference is madeto FIGS. 1 and 3 throughout the description of FIG. 37. At block 632, anode 16 of cluster of nodes 14 modifies at least one boot-time parameterof the node 16 based on a boot-time configuration adjustment requestprovided by cloud configuration server 12. In the illustratedembodiment, the boot-time configuration adjustment request is providedin a configuration file 28 (FIG. 3) and identifies a requestedmodification to one or more boot-time parameters of node 16 based onuser selections made via inputs 270, 271 and fields 268, 270, 272, 274,276 of FIG. 10, described herein. In the illustrated embodiment, thenode 16 has an initial boot-time configuration prior to the modifyingthe at least one boot-time parameter and a modified boot-timeconfiguration following the modifying the at least one boot-timeparameter. The modified boot-time configuration provides at least one ofa reduced computing capacity and a reduced memory capacity of the node16, as described herein.

At block 634, node 16 executes, following a reboot of the node 16 by thenode 16, at least a portion of a workload upon a determination by thenode 16 following the reboot of the node 16 that the at least oneboot-time parameter has been modified according to the boot-timeconfiguration adjustment request. In one embodiment, node 16 obtains theat least a portion of the workload from cloud configuration server 12and executes the workload based on the modification to the at least oneboot-time parameter. In one embodiment, the determination by node 16 isbased on a flag (e.g., one or more bits) set by the node 16 followingthe modification to the at least one boot-time parameter and prior tothe reboot of the node 16. A set flag indicates to the node 16 followinga restart of the node 16 that the at least one boot-time parameter hasalready been modified, and thus node 16 does not attempt to modify theat least one boot-time parameter and reboot again. In one embodiment,the determination is based on a comparison of a boot-time configurationof the node 16 and a requested boot-time configuration identified withthe boot-time configuration adjustment request. For example, node 16compares the current boot-time parameters of the node 16 with therequested boot-time parameters identified with the boot-timeconfiguration adjustment request and, if the parameters are the same,does not attempt to modify the at least one boot-time parameter andreboot again. In one embodiment, when node 16 receives a newconfiguration file containing a new boot-time configuration adjustmentrequest, node 16 clears the flag before implementing the modification tothe boot-time parameters according to the new boot-time configurationadjustment request.

FIG. 38 illustrates a flow diagram 650 of an exemplary detailedoperation performed by cloud computing system 10 for configuring aboot-time configuration of one or more nodes 16 of node cluster 14.Reference is made to FIGS. 1 and 3 throughout the description of FIG.38. In the illustrated embodiment, configurator 22 performs blocks652-656 of FIG. 38, and each configured node 16 performs blocks 658-664of FIG. 38. At block 652, configurator 22 creates one or more boot-timeconfiguration files 28 (FIG. 3) for corresponding nodes 16 based onuser-defined boot-time parameters entered via user interface 200 (FIG.10), as described herein. In one embodiment, the boot-time configurationfile 28 is a patch for one or more configuration files of the node 16 oris in a task-specific file/data format. At block 654, configurator 22starts the cluster of nodes 14 (e.g., upon user selection of input 560,or inputs 562, 564, of FIG. 30, as described herein). At block 656,configurator 22 distributes the boot-time configuration file(s) to theappropriate nodes 16 of the cluster of nodes 14. In one embodiment, eachnode 16 receives a boot-time configuration file, and each file mayidentify unique boot-time parameters for the respective node 16. In oneembodiment, the configuration files 28 are pushed to the nodes, such asvia a secure shell (SSH) file transfer, via an ftp client, via a userdata string in Amazon AWS, or via another suitable file transfermechanism. In another embodiment, the nodes 16 each query (e.g., via anHTTP request) control server 12 or master node 16 for the boot-timeconfiguration information. At block 658, the node 16 applies the desiredboot-time parameter changes specified in the received boot-timeconfiguration file 28. In one example, the node 16 applies a patch tothe boot files of the node 16, or the node 16 uses a utility to generatea new set of boot files for the node 16 based on the boot-timeparameters specified in the received boot-time configuration file 28. Inone embodiment, during or upon applying the desired boot-time changes atblock 658, node 16 sets a status flag that indicates that the boot-timeconfiguration has been updated, as described herein. At block 660, thenode 16 forces a reboot following the application of the boot-timeconfiguration changes. Upon rebooting, node 16 determines at block 662that the boot-time configuration of the node 16 has already been updatedwith the boot-time parameter changes specified in the received boot-timeconfiguration file 28. In one embodiment, node 16 determines theboot-time configuration is updated at block 662 based on the status flagset at block 658 or based on a comparison of the current boot-timeconfiguration of the node 16 to the boot-time configuration file 28, asdescribed herein. As such, node 16 reduces the likelihood of applyingthe boot-time configuration changes more than once. At block 664, node16 proceeds with the execution of other tasks, including execution ofthe workload or the portion of the workload received from control server12.

Modifying and/or Emulating a Network Configuration

FIG. 39 illustrates a flow diagram 700 of an exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for modifying a networkconfiguration of the allocated cluster of nodes 14 of cloud computingsystem 10. Reference is made to FIGS. 1 and 3 as well as FIGS. 11-17throughout the description of FIG. 39. At block 702, networkconfigurator 74 modifies, based on a user selection received via userinterface 200, a network configuration of at least one node 16 ofcluster of nodes 14 of the cloud computing system 10. Modifying thenetwork configuration of the at least one node 16 at block 702 comprisesmodifying the network performance of the at least one node 16 oncommunication network 18 (FIG. 1). The network performance is modifiedby modifying network parameters such as the packet communication rate,dropped or corrupted packets, reordered packets, etc., as describedherein. In the illustrated embodiment, network configurator 74 modifiesthe network configuration of a node 16 by generating a networkconfiguration file 28 (FIG. 3) based on the user selections and inputprovided via module 280 of user interface 200, described herein withrespect to FIGS. 11-17, and by providing the network configuration file28 to the node 16 (or the node 16 fetching the file 28). Nodes 16 thenimplement the changes to the network configuration of the node 16specified in the accessed network configuration file 28. In theillustrated embodiment, the at least one node 16 has an initial networkconfiguration prior to the modifying and a modified networkconfiguration following the modifying. In one embodiment, the modifiednetwork configuration reduces network performance of the at least onenode 16 on the communication network 18 during an execution of theselected workload. Alternatively, the modified network configurationincreases network performance of the at least one node 16, such as, forexample, by decreasing the communication delay value specified via field302 of FIG. 11.

In one embodiment, network configurator 74 modifies the networkconfiguration of the at least one node 16 by changing at least onenetwork parameter of the at least one node 16 to limit the networkperformance of the at least one node 16 on communication network 18during an execution of the workload. In one embodiment, the at least onenetwork parameter that is changed comprises at least one of a packetcommunication delay, a packet loss rate, a packet duplication rate, apacket corruption rate, a packet reordering rate, and a packetcommunication rate, which are selectable by a user via tabs 282-294, asdescribed herein. As such, network configurator 74 limits the networkperformance of the at least one node 16 by generating and providing thenode 16 access to a configuration file 28 that identifies a modificationto a network parameter (e.g., an increased communication delay betweennodes 16, an increased packet loss rate or corruption rate, etc.).

In the illustrated embodiment, configurator 22 provides user interface200 comprising selectable network configuration data, and networkconfigurator 74 modifies the network configuration of the at least onenode 16 based on at least one user selection of the selectable networkconfiguration data, as described herein. Exemplary selectable networkconfiguration data includes inputs 298-301 and corresponding fields302-312 of FIG. 11, inputs 313, 314 and corresponding fields 315, 316 ofFIG. 12, inputs 317, 318 and corresponding fields 319, 320 of FIG. 13,input 321 and corresponding field 322 of FIG. 14, inputs 323, 324 andcorresponding fields 325, 326 of FIG. 15, inputs 327-330, 335-338 andcorresponding fields 331-334 of FIG. 16, and input 340 and correspondingfield 342 of FIG. 17. In one embodiment, network configurator 74modifies the network performance by changing (i.e., via the networkconfiguration file 28), based on at least one user selection of theselectable network configuration data, a first network parameter of afirst node 16 of cluster of nodes 14 to limit the network performance ofthe first node 16 on the communication network 18 during the executionof the workload and by changing a second network parameter of a secondnode 16 of cluster of nodes 14 to limit the network performance of thesecond node 16 on the communication network 18 during the execution ofthe workload. In one embodiment, the first network parameter isdifferent from the second network parameter. As such, networkconfigurator 74 is operative to modify different network parameters ofdifferent nodes 16 of cluster of nodes 14 to achieve desired networkcharacteristics of cluster of nodes 14 during workload execution.

In the illustrated embodiment, configurator 22 is further operative toselect a cluster of nodes 14 for cloud computing system 10 having anetwork configuration that substantially matches a network configurationof an emulated node cluster, as described herein with respect to FIGS.40-42. As referenced herein, an emulated node cluster includes any groupof networked nodes that has a known network configuration that is to beemulated by the node cluster 14 selected by control server 12. Each nodeof the emulated node cluster includes one or more processing devices andmemory accessible by the processing devices. In one embodiment, theemulated node cluster does not include the available nodes 16 selectableby configurator 22. For example, the emulated node cluster includesnodes that are separate from the available nodes 16 housed in the one ormore data center(s) and accessible by configurator 22, such as nodesthat are provided by a user. Alternatively, the emulated node clustermay include a group of the available nodes 16. The network topology andnetwork performance characteristics of the emulated node cluster isobtained using one or more network performance tests, as describedbelow. Referring to FIG. 40, a flow diagram 710 of an exemplaryoperation performed by configurator 22 of FIGS. 1 and 3 is illustratedfor selecting a cluster of nodes 14 that have network characteristicssubstantially matching network characteristics of an emulated nodecluster. Reference is made to FIGS. 1 and 3 throughout the descriptionof FIG. 40. In the illustrated embodiment, configurator 22 selects andconfigures node cluster 14 of FIG. 1 according to the flow diagram 710of FIG. 40 based on user selections received via user interface 200, asdescribed herein. At block 712, node configurator 72 compares acommunication network configuration of an emulated node cluster and anactual communication network configuration of the plurality of availablenodes 16. At block 714, node configurator 72 selects a cluster of nodes14 for cloud computing system 10 from a plurality of available nodes 16coupled to communication network 18 based on the comparison of block712. The selected cluster of nodes 14 include a subset of the pluralityof available nodes 16. At block 716, node configurator 72 configures theselected cluster of nodes 14 to execute a workload such that each node16 of the cluster of nodes 14 is operative to share processing of theworkload with other nodes 16 of the cluster of nodes 14, as describedherein. In one embodiment, blocks 712-716 are initiated upon deploymentof the cloud configuration based on user input to module 216 of FIG. 30,as described herein.

In the illustrated embodiment, the communication network configurationof the emulated node cluster and the actual communication networkconfiguration of the plurality of available nodes 16 each includecommunication network characteristics associated with the correspondingnodes. Node configurator 72 selects the cluster of nodes 14 based onsimilarities between the communication network characteristics of theemulated node cluster and the communication network characteristics ofthe plurality of available nodes 16. Exemplary communication networkcharacteristics include network topology and network parameters.Exemplary network parameters include communication rates and latenciesbetween nodes, network bandwidth between nodes, and packet error rates.Network topology includes the physical and logical connectivity of thenodes, the identification of which nodes and groups of nodes of the nodecluster are physically located near or far from each other, the type ofconnection between the nodes (e.g., fiber optic link, satelliteconnection, etc.), and other suitable characteristics. The packet errorrate includes dropped or lost packets, corrupted packets, reorderedpackets, duplicated packets, etc. In one embodiment, node configurator72 prioritizes the communication network characteristics of the emulatednode cluster and selects the cluster of nodes 14 based on theprioritized communication network characteristics, as described hereinwith respect to FIG. 41.

In the illustrated embodiment, node configurator 72 initiates a networkperformance test on the available nodes 16 to identify the actualcommunication network configuration of the available nodes 16. Anysuitable network performance test may be used. For example, nodeconfigurator 72 may send a request to each available node 16 to executea computer network administration utility such as Packet Internet Groper(“Ping”) to test and collect data regarding the network performancebetween available nodes 16. Based on the results of the Ping testprovided by each node 16, node configurator 72 determines the actualcommunication network configuration of the available nodes 16. In oneembodiment, Ping is used in conjunction with other network performancetests to obtain the actual communication network configuration.Configurator 22 aggregates the network performance test results receivedfrom nodes 16 to create a network descriptor data file or object (seedata file 750 of FIG. 42, for example) that identifies the actualcommunication network configuration of the available nodes 16. In oneembodiment, configurator 22 initiates the network performance test andaggregates the results based on user input to user interface 200. Forexample, a user selection of button 586 of FIG. 30 or another suitableinput may cause configurator 22 to initiate the test and aggregate theresults.

In the illustrated embodiment, node configurator 72 also accesses one ormore data files (e.g., data file 750 of FIG. 42) identifying thecommunication network configuration of the emulated node cluster. In oneembodiment, the data file(s) are obtained offline of control server 12by implementing the one or more network performance tests on theemulated cluster of nodes (e.g., Ping test, etc.). In one embodiment,configurator 22 loads the data file associated with the emulated nodecluster into accessible memory (e.g., memory 90 of FIG. 3). For example,configurator 22 may load the data file based on a user identifying thelocation of the data file via user interface 200, such as via inputs totable 226 of FIG. 7. As such, configurator 22 performs the comparison atblock 712 of FIG. 40 by comparing the communication networkcharacteristics identified in the generated data file associated withthe available nodes 16 and the accessed data file associated with theemulated node cluster.

An exemplary data file 750 is illustrated in FIG. 42. Data file 750identifies a network configuration of any suitable networked nodes, suchas available nodes 16 accessible by control server 12 or nodes of anemulated node cluster. As shown, data file 750 identifies several groupsof nodes illustratively including Groups A, B, . . . M. Each Group A, B,M includes nodes that are physically near each other, such as nodes onthe same physical rack of a data center. Lines 6-11 identify networkparameters associated with network communication by nodes of Group A,lines 15-22 identify network parameters associated with networkcommunication by nodes of Group B, and lines 27-34 identify networkparameters associated with network communication by nodes of Group M.For example, lines 6 and 7 identify a latency, bandwidth, and error rateassociated with communication between nodes of Group A. Lines 8 and 9identify a latency, bandwidth, and error rate associated withcommunication between Group A nodes and Group B nodes. Similarly, lines10 and 11 identify a latency, bandwidth, and error rate associated withcommunication between Group A nodes and Group M nodes. The networkparameters associated with communication by nodes of Groups B and M aresimilarly identified in data file 750. Data file 750 may identifyadditional network configuration data, such as network topology data andother network parameters, as described herein.

Referring to FIG. 41, a flow diagram 720 is illustrated of an exemplarydetailed operation performed by one or more computing devices, includingconfigurator 22 of FIGS. 1 and 3, for selecting a cluster of nodes 14that have network characteristics substantially matching networkcharacteristics of an emulated node cluster. Reference is made to FIGS.1 and 3 throughout the description of FIG. 41. At block 722, a networkconfiguration is requested from each node of the emulated node cluster.For example, the network performance test is initiated on each node, andthe test results are received by the computing device, as describedherein. At block 724, the network configuration data file (e.g., datafile 750) is created based on the network configuration data receivedfrom the nodes of the emulated node cluster resulting from theperformance test. As described herein, blocks 722 and 724 may beperformed offline by a computing system separate from cloud computingsystem 10, such as with computer 20 of FIG. 1, for example.

At block 726, configurator 22 requests a network configuration from eachavailable node 16 or from a group of available nodes 16 of the datacenter. For example, configurator 22 initiates the network performancetest on the available nodes 16, and configurator 22 aggregates theconfiguration data resulting from the network performance tests, asdescribed herein. At block 728, configurator 22 creates a networkconfiguration data file (e.g., data file 750) based on the networkconfiguration data received from available nodes 16. As such,configurator 22 has access to two configuration data files, including adata file describing the emulated node cluster and a data filedescribing the available nodes 16. Configurator 22 selects suitablenodes 16 from the available nodes 16 that have similar networkcharacteristics as the emulated node cluster based on the comparison ofthe network properties identified in the two data files, as representedat block 730. In one embodiment, configurator 22 further selectssuitable nodes at block 730 based on a comparison of the node hardwarecharacteristics (e.g., processing capacity, memory capacity, etc.) ofthe emulated node cluster and the available nodes 16, as describedherein.

At block 732, configurator 22 tunes the selected nodes 16 based on thedesired network configuration parameters identified in the data fileassociated with the emulated node cluster. For example, the networkcharacteristics of the selected nodes 16 may not exactly match thenetwork characteristics of the emulated node cluster, and furthernetwork tuning may be required or desired. As such, the operating system44, network topology driver 48, and/or other network components andnetwork parameters of each node 16 are tuned to further achieve thedesired network performance of the emulated node cluster. In oneembodiment, configurator 22 tunes the selected nodes 16 automaticallybased on the network characteristics identified in the data file. In oneembodiment, network parameters are tuned further based on user inputprovided via module 206 of user interface 200, as described herein withrespect to FIGS. 11-17, for example.

In one exemplary embodiment, configurator 22 selects the suitable nodes16 at block 730 using the following “best matching” technique, althoughother suitable methods and algorithms may be provided. Configurator 22considers Z network properties (i.e., characteristics) when comparingthe network configuration data of the data files (e.g., latency—p₀,bandwidth—p₁, error rate—p_(Z)), and nodes X₁, X₂, . . . , X_(Q) are thenodes on the emulated node cluster. Configurator 22 selects a subset ofavailable nodes 16 (e.g., nodes Y₁, Y₂, . . . , Y_(Q)) that are mostsimilar to nodes X₁, X₂, . . . , X_(Q) with respect to networkproperties p₁, p₂, . . . , p_(x). Although other algorithms may be usedto perform the selection, one exemplary algorithm implemented byconfigurator 22 for findable a suitable subset of available nodes 16includes prioritizing the network properties. In an exemplaryprioritization, property p₀ has higher priority than property p₁, andproperty p_(k) has higher priority than property p_(k+1). As such, inthe illustrated example, latency is given a higher priority thanbandwidth during the node selection, and bandwidth is given a higherpriority than error rate during the node selection. A function P(N,X,Y)with inputs N (network property), X (node), and Y (node) may beconfigured to return the value of network property N between networknodes X and Y. Such a function may be implemented using the networkdescriptor data files/objects (e.g., data files 750) created at blocks724, 728. An initial list of nodes L={Y₁, Y₂, Y₃, . . . } contains allof the available nodes 16. For each node Y_(g) in the cloud where 1≦g≦R(R is the total number of nodes in L, R≧Q), the following equation (1)applies:

Sx(g)=Σ

P(N,Y _(g) ,Y

)  (1)

For each node X_(i) in the emulated node cluster, where 1≦l≦Q (Q is thenumber of nodes in the emulated node cluster), the following equation(2) applies:

Sy(t)=Σ₁

P(N,Y

,Y

)  (2)

The algorithm proceeds to find an available node Y_(w) for the cloudcomputing system 10 such that Sy(w)−Sx(t)=m

n

_(,f)(Sy(v)−Sx(f)). As such, node Y_(w) is used to simulate originalnode X_(i), and node Y_(w) is removed from list L. The algorithmproceeds until a full set of available nodes 16 are selected. Othersuitable methods and algorithms for selecting the nodes 16 at block 730may be provided.

In one exemplary embodiment, configurator 22 tunes the selected nodes 16at block 732 using the following method, although other methods andalgorithms may be provided. With this method, configurator runs aconfiguration application that automatically creates the appropriatenetwork simulation layer on each node 16. If using a Netem network delayand loss emulator, the following algorithm is implemented byconfigurator 22. For each node in the emulated node cluster, G_(s) isthe node group that the emulated node belongs to (i.e., each node groupcomprises nodes that are physically near each other, e.g., same rack).For each group G_(i) where 1<=i<=E and E is the total number of groupsdefined in the data file associated with the emulated node cluster, thefollowing is performed by configurator 22. Configurator 22 looks up thedesired network properties p₀ . . . p_(N) for outgoing traffic from nodeG_(s) to node G_(i). Configurator 22 creates a new class of service,such as by using the command “tc class add dev,” for example.Configurator 22 creates a new queuing discipline, such as by using thecommand “tc qdisc add dev,” for example. Configurator 22 sets thedesired network properties to the class or queuing discipline “qdisc.”The bandwidth and burst network properties are specified at the class,and all other properties (latency, error rate, etc.) are specified atthe queuing discipline. For each node Y_(n), Gy_(n) is the group thatthe node Y_(n) belongs to. Configurator 22 configures a filter based onthe destination IP address (address of node Y_(n)) and assigns it toclass Gy_(n). This can be done, for example, using the command “tcfilter add dev.”

As a result, if the Netem emulator is turned on, the selected nodecluster 14 will have similar network performance to the emulated nodecluster with respect to at least the following network properties:minimum latency, maximum bandwidth, maximum burst rate, minimum packetcorruption rate, minimum packet loss rate, and minimum packet reorderingrate. Other suitable methods and algorithms for tuning the nodes 16 atblock 732 may be provided.

In one embodiment, blocks 726-732 of FIG. 41 are repeated with differentgroups of available nodes 16 until the full cluster of nodes 14 isselected that corresponds to the emulated node cluster. In oneembodiment, the emulated node cluster is theoretical in that thephysical nodes 16 may or may not exist, but the desired networkconfiguration is known and provided as input to configurator 22 forperforming the node selection. In one embodiment, upon selecting thecluster of nodes 14 based on the emulated node cluster, configurator 22is operative to test various workloads with the selected cluster ofnodes 14 having the desired network configuration, such as using batchprocessor 80 described herein.

Allocating a Cluster of Nodes based on Hardware Characteristics

FIG. 43 illustrates a flow diagram 760 of an exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for allocating a clusterof nodes 14 for cloud computing system 10. Reference is made to FIGS.1-3 throughout the description of FIG. 43. At block 762, configurator 22(e.g., data monitor configurator 82) initiates a hardware performanceassessment test on a group of available nodes 16 of one or more datacenters to obtain actual hardware performance characteristics of thegroup of available nodes 16. At block 764, node configurator 72 comparesthe actual hardware performance characteristics of the group ofavailable nodes 16 and desired hardware performance characteristicsidentified based on user selections via user interface 200. At block766, node configurator 72 selects a subset of nodes 16 for the cloudcomputing system 10 from the group of available nodes 16 based on thecomparison at block 764. The subset of nodes 16, such as node cluster 14or a group of nodes 16 of the node cluster 14, are operative to shareprocessing of a workload, as described herein. The number of nodes 16 inthe subset of nodes 16 is less than or equal to a number of nodes 16requested by the user for the node cluster 14, as described herein.

In one embodiment, node configurator 72 receives a user request via userinterface 200 requesting a cluster of nodes for the cloud computingsystem 10 having the desired hardware performance characteristics. Theuser request identifies the desired hardware performance characteristicsbased on, for example, user selections of selectable hardwareconfiguration data, such as selection boxes 259, inputs 262, and field256 of FIG. 8 and selectable inputs 265 of FIG. 9. In one embodiment,the fields of table 264 of FIG. 9 are selectable/modifiable to furtheridentify desired hardware performance characteristics. Node configurator72 may identify the desired hardware performance characteristics basedon other suitable selectable inputs and fields of user interface 200.Node configurator 72 selects the group of available nodes 16 for testingwith the hardware performance assessment test based on the user requestof the cluster of nodes and the desired hardware performancecharacteristics identified in the request (e.g., based on hardwaresimilarities between the available nodes 16 and the requested cluster ofnodes). In the illustrated embodiment, the number of nodes 16 of thegroup of available nodes 16 is greater than the number of nodes 16 ofthe cluster of nodes requested with the user request.

An exemplary hardware performance characteristic includes the computerarchitecture of a node 16, such as whether the node 16 has a 64-bitprocessor architecture or a 32-bit processor architecture to support aworkload that requires native 32-bit and/or 64-bit operations. Otherexemplary hardware performance characteristics include a manufacturer ofthe processor(s) 40 of the node 16 (e.g., AMD, Intel, Nvidia, etc.), anoperating frequency of the processor(s) 40 of the node 16, and aread/write performance of the node 16. Still other exemplary hardwareperformance characteristics include: a system memory capacity and a diskspace (storage capacity); number and size of processors 40 of the node16; a cache size of the node 16; available instruction sets of the node16; disk I/O performance, hard drive speed of the node 16; the abilityof the node 16 to support emulating software; the chipset; the type ofmemory of the node 16; the network communication latency/bandwidthbetween nodes 16; and other suitable hardware performancecharacteristics. In the illustrated embodiment, each of these hardwareperformance characteristics may be specified as desired by a user basedon the user request provided via user interface 200. Further, one ormore hardware performance assessment tests are operative to determinethese actual hardware performance characteristics of each selectedavailable node 16.

In one embodiment, node configurator 72 initiates the hardwareperformance assessment test at block 762 by deploying one or morehardware performance assessment tools to each node 16 that are operativeto identify or determine the hardware performance characteristics of thenode 16 and to generate hardware configuration data representative ofthese characteristics. Data aggregator 84 is then operative to aggregatethe hardware performance data provided by the hardware performanceassessment tools such that node configurator 72 can determine the actualhardware performance characteristics of each node 16 based on theaggregated data. An exemplary assessment tool includes a CPUidentification tool (“CPUID”), which is known in the art, that includesan executable operation code for identifying the type of processor(s) ofthe node 16 and various characteristics/features of the processor (e.g.,manufacturer, processor speed and capacity, available memory and diskspace, etc.). Another exemplary monitoring tool includes a software codemodule that when executed by the node 16 is operative to test for aninstruction set extension or instruction type to determine theinstruction set compatible with the node 16 and/or the manufacturer ofthe processor(s). Another exemplary monitoring tool includes softwarecode modules that when executed by the node 16 are operative to testwhether a node 16 has 64-bit or 32-bit architecture. For example, such atest may involve issuing a command or processing request and measuringhow long the processor takes to complete the request. Other suitableassessment tools may be provided.

In one embodiment, the number of nodes 16 of the subset of nodes 16selected at block 766 is less than the number of nodes 16 identified inthe user request. As such, configurator 22 repeats steps 762-766 toobtain additional subsets of nodes 16 until the number of selected nodes16 is equal to the number of nodes 16 requested with the user request.In one embodiment, after selecting the first subset of nodes 16 at block766, node configurator 72 selects a second group of available nodes 16different from the first group of available nodes 16 initially tested atblock 762. Data monitor configurator 82 initiates the hardwareperformance assessment test on the second group of available nodes 16 toobtain actual hardware performance characteristics of the second groupof available nodes 16, and node configurator 72 selects a second subsetof nodes 16 for the cloud computing system 10 from the second group ofavailable nodes 16 based on a comparison by the node configurator 72 ofthe actual hardware performance characteristics of the second group ofavailable nodes and the desired hardware performance characteristics. Inone embodiment, upon the combined number of nodes of the selectedsubsets of nodes 16 being equal to the number of nodes 16 requested withthe user request, node configurator 72 configures the selected subsetsof nodes 16 as the cluster of nodes 14 of cloud computing system 10(i.e., configures the node cluster 14 with user-specified configurationparameters and runs workloads on the node cluster 14, etc.).

Referring to FIG. 44, a flow diagram 770 is illustrated of an exemplarydetailed operation performed by one or more computing devices, includingconfigurator 22 of FIGS. 1 and 3, for selecting a cluster of nodes 14that have hardware characteristics substantially matching desiredhardware characteristics specified by a user. Reference is made to FIGS.1-3 throughout the description of FIG. 44. At block 772, nodeconfigurator 72 receives a user request for N nodes 16 having desiredhardware performance characteristics, where N is any suitable number ofdesired nodes 16. In one embodiment, the user request is based on userselection of selectable hardware configuration data (e.g., FIGS. 8 and9), as described herein with respect to FIG. 43. At block 774, nodeconfigurator 72 requests or reserves N+M nodes 16 from the availablenodes 16 of the accessed data center(s) or cloud. M is any suitablenumber such that the number (N+M) of reserved available nodes 16 exceedsthe number N of requested nodes 16. For example, M may equal N or mayequal twice N. Alternatively, node configurator 72 may request Navailable nodes 16 at block 774. In one embodiment, the (N+M) nodes 16are allocated or reserved using an application specific API (e.g., anAmazon AWS API, an OpenStack API, a custom API, etc.). Node configurator72 requests the available nodes 16 at block 774 (and block 788) based onthe available nodes 16 having similar hardware characteristics as thedesired cluster of nodes. For example, node configurator 72 may reserveavailable nodes 16 that have the same node type (e.g., small, medium,large, x-large, as described herein).

At block 776, data monitor configurator 82 initiates the hardwareperformance assessment test on each reserved node 16 by deploying one ormore hardware performance assessment tools, and data aggregator 84aggregates (e.g., collects and stores) hardware performance dataresulting from the hardware performance assessment tests initiated oneach node 16, as described herein with respect to FIG. 43. In oneembodiment, the hardware performance assessment tools are software codemodules preinstalled at nodes 16 or installed on nodes 16 using SSH,HTTP, or some other suitable protocol/mechanism.

At block 780, node configurator 72 compares the desired hardwareperformance characteristics of the user request (block 772) with theactual hardware performance characteristics resulting from the hardwareperformance assessment tests. Based on similarities in the actual anddesired hardware performance characteristics, node configurator 72 atblock 782 selects X nodes 16 from the (N+M) reserved nodes 16 that bestmatch the desired hardware characteristics, where X is any number thatis less than or equal to the number N of requested nodes 16. Anysuitable algorithm may be used to compare the hardware characteristicsand to select best-matching nodes 16, such as the “best matching”technique described herein with respect to FIG. 41 based on hardwarecharacteristics. At block 784, node configurator 72 releases theremaining unselected available nodes 16 (e.g., (N+M)−X) back to the datacenter(s) or cloud, such as by using application specific APIs, forexample, so that the unselected available nodes 16 are available for usewith other cloud computing systems. Upon the selected number X of nodes16 being less than the requested number N of nodes 16 at block 786, nodeconfigurator 72 requests or reserves additional nodes 16 from the datacenter(s)/cloud at block 788. Configurator 22 then repeats steps 776-786until the total number of selected nodes 16 (i.e., the combined numberof nodes 16 resulting from all iterations of the selection method) isequal to the number N of requested nodes 16. The selected nodes 16 arethen configured as the cluster of nodes 14 for performing the cloudcomputing tasks assigned by the user.

In one embodiment, the method of FIG. 44 operates in conjunction withthe method of FIG. 41 to select a cluster of nodes 14 having desiredhardware characteristics and network characteristics. In one embodiment,the method of FIG. 44 selects nodes 16 further based on the nodes 16having close network proximity. In one embodiment, the hardwarecharacteristics identified with the user request at block 772 areprioritized prior to selecting nodes 16 for the node cluster 14. In oneembodiment, the method of FIG. 44 (and FIG. 43) is run automatically byconfigurator 22 to find a suitable match of the actual selected clusterof nodes 14 with the desired cluster of nodes specified by the user.Alternatively, a user may be given the option by configurator 22 toinitiate the operations of FIGS. 43 and 44 based on selectable inputs ofuser interface 200, for example.

Selecting and/or Modifying a Hardware Configuration of the CloudComputing System

FIG. 45 illustrates a flow diagram 800 of an exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for selecting a hardwareconfiguration of the cluster of nodes 14 of cloud computing system 10.Reference is made to FIGS. 1 and 3 throughout the description of FIG.45. At block 802, node configurator 72 determines, based on a sharedexecution of a workload by cluster of nodes 14 of the cloud computingsystem 10, that at least one node 16 of the cluster of nodes 14 operatedat less than a threshold operating capacity during the shared executionof the workload. The threshold operating capacity is illustrativelybased on the hardware utilization by the at least one node 16, e.g., theutilization of processor 40 and/or memory 42 during workload execution.The threshold operating capacity may be any suitable threshold, such as,for example, a maximum operating capacity (100%) or a 90% operatingcapacity. At block 804, node configurator 72 selects a modified hardwareconfiguration of the cluster of nodes 14 based on the determination atblock 802 such that the cluster of nodes 14 with the modified hardwareconfiguration has at least one of a reduced computing capacity and areduced storage capacity.

In one embodiment, node configurator 72 selects the modified hardwareconfiguration by selecting at least one different node 16 from aplurality of available nodes 16 of the data center and replacing the atleast one node 16 of the cluster of nodes 14 with the at least onedifferent node 16. The different node 16 has at least one of a reducedcomputing capacity and a reduced storage capacity compared with thereplaced node 16 of the cluster of nodes 14. For example, nodeconfigurator 72 selects a different node 16 from the available nodes 16that has a slower processor 40, fewer processing cores, less memorycapacity, or any other suitable reduced hardware characteristic ascompared with the replaced node 16. For example, the replaced node 16has more computing power or memory capacity than is required to processthe workload such that portions of the hardware of the replaced node 16are underutilized during workload execution. In the illustratedembodiment, the different node 16 is selected such that it is operativeto process the workload with a similar performance (e.g., similarexecution speed, etc.) as the one or more replaced nodes 16 but alsomore efficiently due to the reduced computing and/or storage capacity ofthe different node 16. As such, the cluster of nodes 14 modified withthe different node 16 executes the workload more efficiently due to thereduced computing and/or storage capacity of the different node 16 whileexhibiting little or no overall performance loss. For example, the nodecluster 14 executes the workload at a substantially similar speed withthe different node 16 as with the replaced node 16.

In one embodiment, node configurator 72 selects and implements themodified hardware configuration of block 804 by selecting and removingone or more nodes 16 from the cluster of nodes 14 without replacing theremoved nodes 16 with different nodes 16. For example, node configurator72 determines that one or more nodes 16 of the node cluster 14 are notneeded for the remaining nodes 16 of node cluster 14 to execute theworkload with a similar execution performance. Node configurator 72 thusremoves these one or more nodes 16 from the node cluster 14 and releasesthese nodes 16 back to the data center. In one embodiment, nodeconfigurator 72 selects and implements the modified hardwareconfiguration of block 804 by reducing at least one of the computingcapacity and memory capacity of one or more nodes 16 of the node cluster14 (e.g., by adjusting the boot-time parameters described herein).

In the illustrated embodiment, configurator 22 has access to hardwareusage cost data that identifies the hardware usage cost associated withusing various hardware resources (e.g., nodes 16) for the node cluster14. For example, the cloud computing service (e.g., Amazon, OpenStack,etc.) charges a usage cost based on the hardware, such as the computingcapacity and memory capacity, of each selected node 16 of the nodecluster 14. As such, in one embodiment, node configurator 72 selects theat least one different node 16 to replace one or more nodes 16 of thenode cluster 14 further based on a comparison by the node configurator72 of usage cost data associated with using the at least one differentnode 16 in the cluster of nodes 14 and usage cost data associated withusing the at least one replaced node 16 in the cluster of nodes 14. Inone embodiment, node configurator 72 selects the at least one differentnode 16 upon the usage cost of the at least one different node 16 beingless than the usage cost of the replaced node 16. For example, nodeconfigurator 72 calculates the cost of the hardware resources (e.g.,nodes 16) used in the cluster of nodes 14 and determines cost benefitsassociated with potential hardware configuration changes of the clusterof nodes 14. For example, node configurator 72 selects one or moredifferent nodes 16 that will result in a more efficient use of allocatedhardware resources of the node cluster 14 at a lower usage cost and withminimum performance loss. In one embodiment, configurator 22 configuresthe network configuration or other configuration parameters based on asimilar cost analysis.

In the illustrated embodiment, configurator 22 monitors the hardwareutilization of each node 16 by deploying one or more hardwareutilization monitoring tools to each node 16 of the cluster of nodes 14.Execution of the hardware utilization monitoring tools by each node 16is operative to cause the at least one processor 40 of each node 16 tomonitor a utilization or usage of the computer hardware (e.g., processor40, memory 42, memory controller, etc.) during the execution of theworkload. The monitoring tools then cause the nodes 16 to providehardware utilization data accessible by configurator 22 that isassociated with the hardware utilization of each node 16 duringexecution of the workload. Data aggregator 84 of configurator 22 isoperative to aggregate the hardware utilization data provided by eachnode 16 such that configurator 22 determines the hardware utilization ofeach node 16 based on the aggregated hardware utilization data.Exemplary hardware monitoring tools are described herein with respect toMonitoring module 214 of FIGS. 26-29. For example, the IOStat and VMStattools include code modules executable by the node processor 40 tomonitor the percentage of time the processor 40, virtual memory, and/ormemory controller is busy executing instructions or performing I/Ooperations during workload execution, the percentage of time thesecomponents are waiting/stalled during workload execution, and othersuitable utilization parameters. Based on the determined hardwareutilization of a node 16, node configurator 72 may determine that lessmemory and or less computing power is needed for that node 16 than wasinitially requested and allocated and may replace or remove the node 16from the cluster 14, as described herein.

In one embodiment, node configurator 72 displays selectable hardwareconfiguration data on user interface 200 that represents the selectedmodified hardware configuration selected at block 804. Based on userselection of the selectable hardware configuration data, nodeconfigurator 72 modifies the hardware configuration of the cluster ofnodes 14, e.g., replaces or removes a node 16 of the node cluster 14.Exemplary selectable hardware configuration data is illustrated in table258 of FIG. 8 with selectable inputs 259, 262. For example, nodeconfigurator 72 may display the recommended modified hardwareconfiguration of node cluster 14 in table 258 by listing the recommendednodes 16 of the node cluster 14 including one or more different nodes 16or removed nodes 16. The user selects the inputs 259 corresponding tothe listed nodes 16 to accept the hardware changes, and nodeconfigurator 72 configures the modified node cluster 14 based on theaccepted changes upon initiation of workload deployment, describedherein. In one embodiment, the hardware usage cost is also displayedwith user interface 200 for one or more recommended hardwareconfigurations of the node cluster 14 to allow a user to select aconfiguration for implementation based on the associated usage cost.Other suitable interfaces may be provided for displaying the modifiedhardware configuration of the cluster of nodes 14. In one embodiment,node configurator 72 automatically configures the cluster of nodes 14with the modified hardware configuration selected at block 804 withoutuser input or confirmation, and initiates further executions of theworkload with the modified node cluster 14.

Referring to FIG. 46, a flow diagram 810 is illustrated of an exemplarydetailed operation performed by one or more computing devices, includingconfigurator 22 of FIGS. 1 and 3, for selecting a hardware configurationof the cluster of nodes 14 of cloud computing system 10. Reference ismade to FIGS. 1-3 throughout the description of FIG. 46. At block 812,configurator 22 provides user interface 200 including selectable nodedata to allow for user selection at block 814 of a desired cluster ofnodes 14 with a desired hardware configuration, as described herein. Atblock 816, configurator 22 selects and configures the selected clusterof nodes 14 and deploys the workload to the cluster of nodes 14, asdescribed herein. At block 818, configurator 22 installs and/orconfigures the hardware utilization monitoring tools on each node 16 ofthe node cluster 14. In one embodiment, the monitoring tools areselected by a user via Monitoring module 214 of FIGS. 26-29.Alternatively, configurator 22 may automatically deploy one or moremonitoring tools, such as the IOStat and VMStat tools, based oninitiation of the method of FIG. 46. At block 820, workload configurator78 initiates execution of a workload on the cluster of nodes 14, and atblock 822, following or during the execution, data aggregator 84collects and stores the hardware utilization data provided by themonitoring tools of each node 16.

Upon completion of the workload execution by the node cluster 14, nodeconfigurator 72 determines the hardware utilization of each node 16based on the hardware utilization data, as represented with block 824.At block 826, node configurator 72 determines whether the hardwareutilization of each node 16 met or exceeded a utilization threshold(e.g., 100% utilization, 90% utilization, or any other suitableutilization threshold). In one embodiment, node configurator 72 comparesmultiple utilization measurements to one or more utilization thresholdsat block 826, such as processor utilization, memory utilization, memorycontroller utilization, etc. If yes at block 826, the node cluster 14 isdetermined to be suitable for further workload executions, i.e., noadjustments to the hardware configuration of the node cluster 14 aremade by configurator 22. For each node 16 that does not meet or exceedthe utilization threshold at block 826, node configurator 72 identifiesa different, replacement node 16 from the available nodes 16 of the datacenter that has hardware that is suitable for execution of the workload(i.e., similar performance to the replaced node(s) 16) while having lesscomputing or memory capacity as the replaced node 16, as describedherein with respect to FIG. 45. At block 830, node configurator 72provides feedback to a user of any recommended hardware configurationchanges identified at block 828 by displaying the recommended hardwareconfiguration of the cluster of nodes 14 on user interface 200, asdescribed with respect to FIG. 45. At block 832, node configurator 72applies the recommended hardware configuration changes for futureexecutions of the workload by removing and/or replacing nodes 16 of theoriginal node cluster 14 with the different nodes 16 identified at block828.

In one embodiment, a selection by the user of a selectable input of userinterface 200 causes node configurator 72 to run the hardwareconfiguration method described with FIGS. 45 and 46 to find a suitableconfiguration of node cluster 14 for executing the workload.Alternatively, configurator 22 may automatically implement the method ofFIGS. 45 and 46, such as upon initiation of a batch processing job, forexample, to find a suitable alternative configuration of the cluster ofnodes 14 that does not significantly limit workload performance.

Tuning the Cloud Computing System

FIG. 47 illustrates a flow diagram 850 of an exemplary operationperformed by configurator 22 of FIGS. 1 and 3 for selecting a suitableconfiguration of the cluster of nodes 14 of cloud computing system 10from a plurality of available configurations. Reference is made to FIGS.1 and 3 throughout the description of FIG. 47. At block 852,configurator 22 (e.g., batch processor 80) initiates a plurality ofexecutions of a workload on cluster of nodes 14 based on a plurality ofdifferent sets of configuration parameters for the cluster of nodes 14.The configuration parameters, provided as input to nodes 16 byconfigurator 22 (e.g., via one or more configuration files 28 asdescribed herein), are adjustable by configurator 22 to provide thedifferent sets of configuration parameters, and the workload is executedby the cluster of nodes 14 with each different set of configurationparameters. In one embodiment, configurator 22 adjusts the configurationparameters for each workload execution based on user input provided viauser interface 200, as described herein. In one embodiment, theconfiguration parameters include at least one of the following: anoperational parameter of the workload container of at least one node 16,a boot-time parameter of at least one node 16, and a hardwareconfiguration parameter of at least one node 16.

At block 854, node configurator 72 selects a set of configurationparameters for the cluster of nodes 14 from the plurality of differentsets of configuration parameters. At block 856, workload configurator 78provides (e.g., deploys) the workload to the cluster of nodes 14 forexecution by the cluster of nodes 14 configured with the selected set ofconfiguration parameters. As such, future executions of the workload areperformed by the cluster of nodes 14 having a configuration that isbased on the selected set of configuration parameters.

The selection of the set of configuration parameters at block 854 isbased on a comparison by the node configurator 72 of at least oneperformance characteristic of the cluster of nodes 14 monitored (e.g.,with monitoring tools) during each execution of the workload and atleast one desired performance characteristic of the cluster of nodes 14.For example, in one embodiment node configurator 72 selects the set ofconfiguration parameters that result in performance characteristics ofthe node cluster 14 during workload execution that best match desiredperformance characteristics specified by a user. In the illustratedembodiment, the desired performance characteristics are identified bynode configurator 72 based on user input provided via user interface200. For example, user interface 200 includes selectable performancedata, such as selectable inputs or fillable fields, that allow a user toselect desired performance characteristics of the cluster of nodes 14when executing a selected workload. See, for example, fillable field 276of FIG. 10 or any other suitable selectable input or field of userinterface 200 configured to receive user input identifying desiredperformance characteristics. In another example, node configurator 72may load a user-provided file containing data identifying the desiredperformance characteristics, such as based on user selection of inputs238, 228, 230, 232 of FIG. 7 and/or button 494 of the batch processormodule 212 of FIG. 25, for example.

Exemplary performance characteristics specified by the user andmonitored during workload execution include a workload execution time, aprocessor utilization by a node 16, a memory utilization by a node 16, apower consumption by a node 16, a hard disk input/output (I/O)utilization by a node 16, and a network utilization by a node 16. Othersuitable performance characteristics may be monitored and/or specifiedby a user, such as the performance characteristics monitored with themonitoring tools described herein with respect to FIGS. 26-29.

In one embodiment, the selection of the set of configuration parametersat block 854 is further based on a determination by node configurator 72that a value associated with one or more performance characteristicsmonitored during an execution of the workload falls within a range ofacceptable values associated with one or more corresponding desiredperformance characteristics. For example, ranges of acceptable values(e.g., input by a user or set by node configurator 72) associated withcorresponding desired performance characteristics may include 85% to100% processor utilization and 85% to 100% memory utilization.Accordingly, node configurator 72 selects a set of configurationparameters that result in 95% processor utilization and 90% memoryutilization but rejects a set of configuration parameters resulting in80% processor utilization and 75% memory utilization. Upon multiple setsof configuration parameters resulting in performance characteristicsthat meet the acceptable range of values, node configurator 72 selectsthe set of configuration parameters based on additional factors, such asthe best performance values, the lowest usage cost, priorities of theperformance characteristics, or other suitable factors. Upon no sets ofconfiguration parameters resulting in performance characteristics thatfall within the acceptable ranges, node configurator 72 selects the setthat results in the best matching performance characteristics,automatically further adjusts configuration parameters until anappropriate set is found, and/or notifies the user that no sets ofconfiguration parameters were found to be acceptable.

In one embodiment, node configurator 72 assigns a score value to eachdifferent set of configuration parameters based on the similarities ofthe monitored performance characteristics to the desired performancecharacteristics. As such, the selection of the set of configurationparameters at block 854 is further based on the score value assigned tothe selected set of configuration parameters. For example, nodeconfigurator 72 selects the set of configuration parameters resulting inthe highest score value. The score value ranks the sets of configurationparameters based on how closely the performance characteristics of thenode cluster 14 match the desired performance characteristics.

In one embodiment, the selection of the set of configuration parametersat block 854 is further based on a comparison of usage cost dataassociated with using different available nodes 16 or networkconfigurations with the cluster of nodes 14. For example, nodeconfigurator 72 may select a set of configuration parameters that resultin a processor and memory utilization greater than a thresholdutilization level and a usage cost less than a threshold cost level. Anyother suitable considerations of usage cost may be applied to theselection at block 854.

In one embodiment, configurator 22 initiates a first execution of theworkload on node cluster 14 based on an initial set configurationparameters provided by a user (e.g., via user interface 200). In thisembodiment, to find a set of configuration parameters resulting in thedesired performance characteristics, node configurator 72 steps throughdifferent sets of configuration parameters by automatically adjusting atleast one configuration parameter of the initial set and initiatingadditional executions of the workload based on the modified initialsets. Any suitable design space exploration method or algorithm may beused to explore different sets of configuration parameters in thisfashion.

In one embodiment, data monitor aggregator 82 deploys one or more nodeand network performance monitoring tools (described with FIGS. 26-29,for example) to each node 16 of the cluster of nodes 14. The monitoringtools when executed by each node 16 (or by control server 12) areoperative to monitor performance characteristics of each node 16 duringeach execution of the workload, as described herein. The executedmonitoring tools generate performance data representing the performancecharacteristics of the corresponding node 16 that are accessible byconfigurator 22. Data aggregator 84 aggregates the performance dataprovided by the performance monitoring tools of each node 16, and nodeconfigurator 72 selects the set of configuration parameters at block 854based on the aggregated performance data.

As described herein, the different sets of configuration parameters ofthe cluster of nodes 14 include at least one of an operational parameterof the workload container, a boot-time parameter, and a hardwareconfiguration parameter. Exemplary operational parameters of theworkload container are described herein with respect to FIGS. 4-6, 19,and 20 and include, for example, operational parameters associated withat least one of a read/write operation, a file system operation, anetwork socket operation, and a sorting operation. The operationalparameters are selected and modified by workload container configurator76 based on user selections of the selectable data (e.g., inputs andfields) illustrated in FIGS. 19 and 20 and described herein. Exemplaryoperational parameters associated with the read/write operation includea memory buffer size for the read/write operation and a size of a datablock transferred during the read/write operation. Exemplary operationalparameters associated with the file system operation comprises at leastone of a number of file system records stored in memory of each node 16and a number of processing threads of each node 16 allocated forprocessing requests for the file system. An exemplary operationalparameter associated with the sorting operation includes a number ofdata streams to merge when performing the sorting operation. Othersuitable operational parameters of a workload container may be provided.

Exemplary boot-time parameters are described herein with respect toFIGS. 10 and 36-38 and include, for example, a number of processingcores of a node 16 that are enabled during an execution of the workloadand an amount of system memory of a node 16 that is accessible by anoperating system 44 of the node 16. The boot-time parameters areselected and modified by node configurator 72 based on user selection ofthe selectable data (e.g., inputs and fields) illustrated in FIG. 10 anddescribed herein. Other suitable boot-time parameters may be provided.Exemplary hardware configuration parameters are described herein withrespect to FIGS. 8, 9, and 43-46 and include, for example, at least oneof a number of processors 40 of a node 16, an amount of system memory ofa node 16, and an amount of hard disk space of a node 16. The hardwareconfiguration parameters are selected and modified by node configurator72 based on user selection of the selectable data (e.g., inputs andfields) illustrated in FIGS. 8 and 9 and described herein. Othersuitable hardware configuration parameters may be provided.

Referring to FIG. 48, a flow diagram 860 is illustrated of an exemplarydetailed operation performed by one or more computing devices, includingconfigurator 22 of FIGS. 1 and 3, for selecting a suitable configurationof the cluster of nodes 14 of cloud computing system 10 from a pluralityof available configurations. Reference is made to FIGS. 1-3 throughoutthe description of FIG. 48. In the illustrated embodiment of FIG. 48,configurator 22 stops searching for a suitable set of configurationparameters upon the actual performance of the node cluster 14 meeting orexceeding the desired performance. In another embodiment, configurator22 tries each set of identified configuration parameters beforeselecting a set of configuration parameters that are a best match basedon the desired performance characteristics and/or other suitable factors(e.g., usage cost).

At block 862, configurator 22 receives one or more sets of configurationparameters as well as the desired performance characteristics associatedwith the workload execution based on user input received via userinterface 200, as described herein. At block 864, configurator 22allocates a cluster of nodes 14 and configures the cluster of nodes 14with a set of configuration parameters received at block 862. In oneembodiment, configurator 22 deploys one or more configuration files 28to nodes 16 identifying the configuration parameters at block 864, asdescribed herein. Configurator 22 installs and/or configures one or moremonitoring tools (e.g., selected by a user via module 214, for example)on each node 16 at block 866 and initiates an execution of the workloadby the cluster of nodes 14 at block 868. Upon or during execution of theworkload, configurator 22 aggregates the performance data generated bythe one or more monitoring tools of each node 16 at block 870. Based onthe aggregated performance data, at block 872 configurator 22 comparesthe desired performance characteristics identified at block 862 with theactual performance characteristics of the cluster 14 identified with theaggregated performance data, as described herein. At block 874,configurator 22 determines if the performance characteristics aresuitable as compared with the desired performance characteristics (e.g.,within an acceptable range, having a suitable score value, etc.), asdescribed herein. If yes at block 874, configurator keeps the currentconfiguration parameters last implemented at block 864 for futureexecutions of the workload. If the performance characteristics are notas desired at block 874 and if the available different sets ofconfiguration parameters are not exhausted at block 876, configurator 22selects a different set of configuration parameters at block 878, andrepeats the functions of blocks 864-876. For example, configurator 22may implement a different set of configuration parameters identified atblock 862 or an incrementally adjusted set of parameters provided byconfigurator 22, as described above. The process repeats until theconfigurator 22 finds a suitable set of configuration parameters atblock 874 or the configuration parameter options are exhausted at block876. If the configuration options are exhausted at block 876,configurator 22 selects the set of configuration parameters thatprovided the best performance characteristics and other identifiedcharacteristics (e.g., usage cost) at block 880.

Among other advantages, the method and system allow for the selectionand modification of a boot-time configuration of one or more nodes of acluster of nodes of a cloud computing system based on desired boot-timeparameters. In addition, the method and system provide for thedeployment of the selected boot-time configuration to the cluster ofnodes of the cloud computing system such that the cluster of nodesadjusts its boot-time parameters based on the selected boot-timeconfiguration. Other advantages will be recognized by those of ordinaryskill in the art.

While this invention has been described as having preferred designs, thepresent invention can be further modified within the spirit and scope ofthis disclosure. This application is therefore intended to cover anyvariations, uses, or adaptations of the invention using its generalprinciples. Further, this application is intended to cover suchdepartures from the present disclosure as come within known or customarypractice in the art to which this disclosure pertains and which fallwithin the limits of the appended claims.

What is claimed is:
 1. A method of configuring a computing systemcarried out by one or more computing devices, the method comprising:providing a user interface comprising selectable boot-time configurationdata; selecting, based on at least one user selection of the selectableboot-time configuration data, a boot-time configuration for at least onenode of a cluster of nodes of the computing system, each node of thecluster of nodes being operative to share processing of a workload withother nodes of the cluster of nodes; and configuring the at least onenode of the cluster of nodes with the selected boot-time configurationto modify at least one boot-time parameter of the at least one node. 2.The method of claim 1, wherein the selecting comprises selecting, basedon at least one user selection of the selectable boot-time configurationdata, a first boot-time configuration for a first node of the cluster ofnodes and a second boot-time configuration for a second node of thecluster of nodes, the first boot-time configuration being different fromthe second boot-time configuration, and wherein the configuringcomprises configuring the first node with the first boot-timeconfiguration to modify at least one boot-time parameter of the firstnode and configuring the second node with the second boot-timeconfiguration to modify at least one boot-time parameter of the secondnode.
 3. The method of claim 1, wherein the at least one boot-timeparameter comprises at least one of a number of processing cores of theat least one node that are enabled during an execution of the workloadand an amount of system memory of the at least one node that isaccessible by an operating system of the node.
 4. The method of claim 1,further comprising selecting, based on at least one user selectionreceived via the user interface, the workload for execution on thecluster of nodes, wherein the processing of the selected workload isdistributed across the cluster of nodes.
 5. The method of claim 4,wherein the workload comprises a plurality of instructions for executionby the cluster of nodes, and wherein the at least one boot-timeparameter identifies a subset of the plurality of instructions of theworkload to be executed by the at least one node.
 6. The method of claim4, further comprising initiating an execution of the selected workloadwith the cluster of nodes such that the cluster of nodes execute theselected workload with at least one of a reduced computing capacity anda reduced memory capacity based on the modified at least one boot-timeparameter of the at least one node.
 7. A computing configuration systemcomprising: a node configurator operative to select, based on at leastone user selection of selectable boot-time configuration data of a userinterface, a boot-time configuration for at least one node of a clusterof nodes of a computing system, and configure the at least one node ofthe cluster of nodes with the selected boot-time configuration to modifyat least one boot-time parameter of the at least one node such thatshared processing of a workload by the cluster of nodes is based on themodified at least one boot-time parameter.
 8. The system of claim 7, thenode configurator being further operative to select, based on at leastone user selection of the selectable boot-time configuration data, afirst boot-time configuration for a first node of the cluster of nodesand a second boot-time configuration for a second node of the cluster ofnodes, the first boot-time configuration being different from the secondboot-time configuration, to configure the first node with the firstboot-time configuration to modify at least one boot-time parameter ofthe first node, and to configure the second node with the secondboot-time configuration to modify at least one boot-time parameter ofthe second node.
 9. The system of claim 7, wherein the user interfacecomprises a graphical user interface comprising the selectable boot-timeconfiguration data, the system comprising at least one processor andmemory containing executable instructions that when executed by the atleast one processor cause the at least one processor to provide thegraphical user interface on a display.
 10. The system of claim 7,further comprising a workload configurator operative to select, based onat least one user selection received via the user interface, theworkload for execution on the cluster of nodes, and initiate anexecution of the selected workload by the cluster of nodes such that thecluster of nodes execute the selected workload with at least one of areduced computing capacity and a reduced memory capacity based on themodified at least one boot-time parameter of the at least one node. 11.The system of claim 7, wherein the at least one boot-time parametercomprises at least one of a number of processing cores of the at leastone node that are enabled during an execution of the workload by thecluster of nodes and an amount of system memory of the at least one nodethat is accessible by an operating system of the at least one node. 12.The system of claim 7, wherein the workload comprises a plurality ofinstructions for execution by the cluster of nodes, and wherein the atleast one boot-time parameter identifies a subset of the plurality ofinstructions of the workload to be executed by the at least one node.13. A cloud computing configuration method carried out by a node of acluster of nodes of a cloud computing system, the method comprising:modifying, by the node of the cluster of nodes, at least one boot-timeparameter of the node based on a boot-time configuration adjustmentrequest provided by a cloud configuration server, each node of thecluster of nodes comprising at least one processing device and memoryand being operative to share processing of a workload with other nodesof the cluster of nodes; and executing, by the node following a rebootof the node, at least a portion of the workload upon a determination bythe node following the reboot of the node that the at least oneboot-time parameter has been modified according to the boot-timeconfiguration adjustment request.
 14. The method of claim 13, whereinthe determination is based on a flag set by the node following themodifying the at least one boot-time parameter and prior to the rebootof the node, the flag indicating that the at least one boot-timeparameter has been modified.
 15. The method of claim 13, wherein thedetermination is based on a comparison of a boot-time configuration ofthe node and a requested boot-time configuration identified with theboot-time configuration adjustment request.
 16. The method of claim 13,wherein the node has an initial boot-time configuration prior to themodifying the at least one boot-time parameter and a modified boot-timeconfiguration following the modifying the at least one boot-timeparameter, and wherein the modified boot-time configuration provides atleast one of a reduced computing capacity and a reduced memory capacityof the node.
 17. The method of claim 13, wherein the at least oneboot-time parameter comprises at least one of a number of processingcores of the node that are enabled during an execution of the workloadand an amount of system memory of the node that is accessible by anoperating system of the node.
 18. The method of claim 13, wherein theworkload comprises a plurality of instructions for execution by thecluster of nodes, and wherein the at least one boot-time parameteridentifies a subset of the plurality of instructions of the workload tobe executed by the node.
 19. The method of claim 13, further comprisingobtaining, by the node, the at least a portion of the workload from thecloud configuration server for execution by the node; and executing, bythe node, the at least a portion of the workload based on the modifiedat least one boot-time parameter.
 20. A cloud computing systemcomprising: a cloud configuration server operative to generate aboot-time configuration adjustment request; and a cluster of nodes incommunication with the cloud configuration server and operative toexecute a workload, each node of the cluster of nodes comprising atleast one processing device and memory and being operative to shareprocessing of the workload with other nodes of the cluster of nodes, anode of the cluster of nodes being operative to: modify at least oneboot-time parameter of the node based on the boot-time configurationadjustment request provided with the cloud configuration server, andexecute, following a reboot of the node by the node, at least a portionof the workload upon a determination by the node following the reboot ofthe node that the at least one boot-time parameter has been modifiedaccording to the boot-time configuration adjustment request.
 21. Thesystem of claim 20, wherein the determination by the node is based on aflag set by the node following the modification of the at least oneboot-time parameter and prior to the reboot of the node, the flagindicating that the at least one boot-time parameter has been modified.22. The system of claim 20, wherein the determination by the node isbased on a comparison of a boot-time configuration of the node and arequested boot-time configuration identified with the boot-timeconfiguration adjustment request.
 23. The system of claim 20, whereinthe node has an initial boot-time configuration prior to themodification of the at least one boot-time parameter and a modifiedboot-time configuration following the modification of the at least oneboot-time parameter, and wherein the modified boot-time configurationprovides at least one of a reduced computing capacity and a reducedmemory capacity of the node.
 24. The system of claim 20, wherein the atleast one boot-time parameter comprises at least one of a number ofprocessing cores of the node that are enabled during an execution of theworkload and an amount of system memory of the node that is accessibleby an operating system of the node.
 25. The system of claim 20, whereinthe node is further operative to obtain the at least a portion of theworkload from the cloud configuration server for execution by the node,and execute the at least a portion of the workload based on the modifiedat least one boot-time parameter.